Rebuilding encoded data slices in a dispersed storage network

ABSTRACT

A method for a computing device to rebuild a plurality of to-be rebuilt encoded data slices in a dispersed storage network (DSN) begins with the computing device, for each set of encoded data slices of a plurality of sets of encoded data slices that includes at least one of the plurality of to-be rebuilt encoded data slices, determining a cumulative memory health for memory devices of storage units storing other encoded data slices of the respective set of encoded data slices and determining a probability of data loss. The method continues with the computing device prioritizing rebuilding based on the probability of data loss for each set of encoded data slices and rebuilding, in accordance with the prioritizing, a first to-be rebuilt encoded data slice of the plurality of to-be rebuilt encoded data slices to produce a first rebuilt encoded data slice.

CROSS REFERENCE TO RELATED PATENTS

The present U.S. Utility patent application claims priority pursuant to35 U.S.C. § 119(e) to U.S. Provisional Application No. 62/121,736,entitled “TRANSITIONING A STATE OF A DISPERSED STORAGE NETWORK,” filedFeb. 27, 2015, which is hereby incorporated herein by reference in itsentirety and made part of the present U.S. Utility patent applicationfor all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

NOT APPLICABLE

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

NOT APPLICABLE

BACKGROUND OF THE INVENTION

Technical Field of the Invention

This invention relates generally to computer networks and moreparticularly to dispersed storage of data and distributed taskprocessing of data.

Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc., on an Internetstorage system. The Internet storage system may include a RAID(redundant array of independent disks) system and/or a dispersed storagesystem that uses an error correction scheme to encode data for storage.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a distributedcomputing system in accordance with the present invention;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present invention;

FIG. 3 is a diagram of an example of a distributed storage and taskprocessing in accordance with the present invention;

FIG. 4 is a schematic block diagram of an embodiment of an outbounddistributed storage and/or task (DST) processing in accordance with thepresent invention;

FIG. 5 is a logic diagram of an example of a method for outbound DSTprocessing in accordance with the present invention;

FIG. 6 is a schematic block diagram of an embodiment of a dispersederror encoding in accordance with the present invention;

FIG. 7 is a diagram of an example of a segment processing of thedispersed error encoding in accordance with the present invention;

FIG. 8 is a diagram of an example of error encoding and slicingprocessing of the dispersed error encoding in accordance with thepresent invention;

FIG. 9 is a diagram of an example of grouping selection processing ofthe outbound DST processing in accordance with the present invention;

FIG. 10 is a diagram of an example of converting data into slice groupsin accordance with the present invention;

FIG. 11 is a schematic block diagram of an embodiment of a DST executionunit in accordance with the present invention;

FIG. 12 is a schematic block diagram of an example of operation of a DSTexecution unit in accordance with the present invention;

FIG. 13 is a schematic block diagram of an embodiment of an inbounddistributed storage and/or task (DST) processing in accordance with thepresent invention;

FIG. 14 is a logic diagram of an example of a method for inbound DSTprocessing in accordance with the present invention;

FIG. 15 is a diagram of an example of de-grouping selection processingof the inbound DST processing in accordance with the present invention;

FIG. 16 is a schematic block diagram of an embodiment of a dispersederror decoding in accordance with the present invention;

FIG. 17 is a diagram of an example of de-slicing and error decodingprocessing of the dispersed error decoding in accordance with thepresent invention;

FIG. 18 is a diagram of an example of a de-segment processing of thedispersed error decoding in accordance with the present invention;

FIG. 19 is a diagram of an example of converting slice groups into datain accordance with the present invention;

FIG. 20 is a diagram of an example of a distributed storage within thedistributed computing system in accordance with the present invention;

FIG. 21 is a schematic block diagram of an example of operation ofoutbound distributed storage and/or task (DST) processing for storingdata in accordance with the present invention;

FIG. 22 is a schematic block diagram of an example of a dispersed errorencoding for the example of FIG. 21 in accordance with the presentinvention;

FIG. 23 is a diagram of an example of converting data into pillar slicegroups for storage in accordance with the present invention;

FIG. 24 is a schematic block diagram of an example of a storageoperation of a DST execution unit in accordance with the presentinvention;

FIG. 25 is a schematic block diagram of an example of operation ofinbound distributed storage and/or task (DST) processing for retrievingdispersed error encoded data in accordance with the present invention;

FIG. 26 is a schematic block diagram of an example of a dispersed errordecoding for the example of FIG. 25 in accordance with the presentinvention;

FIG. 27 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module storing a plurality ofdata and a plurality of task codes in accordance with the presentinvention;

FIG. 28 is a schematic block diagram of an example of the distributedcomputing system performing tasks on stored data in accordance with thepresent invention;

FIG. 29 is a schematic block diagram of an embodiment of a taskdistribution module facilitating the example of FIG. 28 in accordancewith the present invention;

FIG. 30 is a diagram of a specific example of the distributed computingsystem performing tasks on stored data in accordance with the presentinvention;

FIG. 31 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module storing data and taskcodes for the example of FIG. 30 in accordance with the presentinvention;

FIG. 32 is a diagram of an example of DST allocation information for theexample of FIG. 30 in accordance with the present invention;

FIGS. 33-38 are schematic block diagrams of the DSTN module performingthe example of FIG. 30 in accordance with the present invention;

FIG. 39 is a diagram of an example of combining result information intofinal results for the example of FIG. 30 in accordance with the presentinvention;

FIG. 40A is a schematic block diagram of an embodiment of a dispersedstorage network (DSN) in accordance with the present invention;

FIG. 40B is a flowchart illustrating an example of transitioning a stateof a dispersed storage network in accordance with the present invention;

FIGS. 41A-B are schematic block diagrams of another embodiment of adispersed storage network (DSN) in accordance with the presentinvention;

FIG. 41C is a flowchart illustrating an example of rebuilding encodeddata slices in accordance with the present invention;

FIG. 42A is a schematic block diagram of another embodiment of adispersed storage network (DSN) in accordance with the presentinvention;

FIG. 42B is a flowchart illustrating an example of selecting retrievallocations in accordance with the present invention;

FIG. 43A is a schematic block diagram of an embodiment of a dispersedstorage network (DSN) in accordance with the present invention;

FIG. 43B is a flowchart illustrating an example of accessing datautilizing a plurality of storage pools in accordance with the presentinvention;

FIGS. 44A and 44B are schematic block diagrams of another embodiment ofa dispersed storage network (DSN) in accordance with the presentinvention;

FIG. 44C is a flowchart illustrating an example of accessing data inaccordance with the present invention;

FIG. 45A is a schematic block diagram of another embodiment of adispersed storage network (DSN) in accordance with the presentinvention;

FIG. 45B is a diagram illustrating an example of matrix multiplicationof an encoding matrix and a data matrix to produce a coded matrix inaccordance with the present invention;

FIG. 45C is a flowchart illustrating an example of accessing seriallystored data in accordance with the present invention;

FIGS. 46A and 46B are schematic block diagrams of another embodiment ofa dispersed storage network (DSN) in accordance with the presentinvention;

FIG. 46C is a flowchart illustrating an example of optimizing datastorage in accordance with the present invention;

FIGS. 47A, 47B, and 47C are schematic block diagrams of anotherembodiment of a dispersed storage network (DSN) in accordance with thepresent invention;

FIG. 47D is a flowchart illustrating another example of optimizing datastorage in accordance with the present invention;

FIGS. 48A, 48B, and 48C are schematic block diagrams of an embodiment ofa dispersed storage network (DSN) in accordance with the presentinvention; and

FIG. 48D is a flowchart illustrating an example of transitioning to anoptimized data storage approach in accordance with the presentinvention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a distributedcomputing system 10 that includes a user device 12 and/or a user device14, a distributed storage and/or task (DST) processing unit 16, adistributed storage and/or task network (DSTN) managing unit 18, a DSTintegrity processing unit 20, and a distributed storage and/or tasknetwork (DSTN) module 22. The components of the distributed computingsystem 10 are coupled via a network 24, which may include one or morewireless and/or wire lined communication systems; one or more privateintranet systems and/or public internet systems; and/or one or morelocal area networks (LAN) and/or wide area networks (WAN).

The DSTN module 22 includes a plurality of distributed storage and/ortask (DST) execution units 36 that may be located at geographicallydifferent sites (e.g., one in Chicago, one in Milwaukee, etc.). Each ofthe DST execution units is operable to store dispersed error encodeddata and/or to execute, in a distributed manner, one or more tasks ondata. The tasks may be a simple function (e.g., a mathematical function,a logic function, an identify function, a find function, a search enginefunction, a replace function, etc.), a complex function (e.g.,compression, human and/or computer language translation, text-to-voiceconversion, voice-to-text conversion, etc.), multiple simple and/orcomplex functions, one or more algorithms, one or more applications,etc.

Each of the user devices 12-14, the DST processing unit 16, the DSTNmanaging unit 18, and the DST integrity processing unit 20 include acomputing core 26 and may be a portable computing device and/or a fixedcomputing device. A portable computing device may be a social networkingdevice, a gaming device, a cell phone, a smart phone, a personal digitalassistant, a digital music player, a digital video player, a laptopcomputer, a handheld computer, a tablet, a video game controller, and/orany other portable device that includes a computing core. A fixedcomputing device may be a personal computer (PC), a computer server, acable set-top box, a satellite receiver, a television set, a printer, afax machine, home entertainment equipment, a video game console, and/orany type of home or office computing equipment. User device 12 and DSTprocessing unit 16 are configured to include a DST client module 34.

With respect to interfaces, each interface 30, 32, and 33 includessoftware and/or hardware to support one or more communication links viathe network 24 indirectly and/or directly. For example, interface 30supports a communication link (e.g., wired, wireless, direct, via a LAN,via the network 24, etc.) between user device 14 and the DST processingunit 16. As another example, interface 32 supports communication links(e.g., a wired connection, a wireless connection, a LAN connection,and/or any other type of connection to/from the network 24) between userdevice 12 and the DSTN module 22 and between the DST processing unit 16and the DSTN module 22. As yet another example, interface 33 supports acommunication link for each of the DSTN managing unit 18 and DSTintegrity processing unit 20 to the network 24.

The distributed computing system 10 is operable to support dispersedstorage (DS) error encoded data storage and retrieval, to supportdistributed task processing on received data, and/or to supportdistributed task processing on stored data. In general and with respectto DS error encoded data storage and retrieval, the distributedcomputing system 10 supports three primary operations: storagemanagement, data storage and retrieval (an example of which will bediscussed with reference to FIGS. 20-26), and data storage integrityverification. In accordance with these three primary functions, data canbe encoded, distributedly stored in physically different locations, andsubsequently retrieved in a reliable and secure manner. Such a system istolerant of a significant number of failures (e.g., up to a failurelevel, which may be greater than or equal to a pillar width minus adecode threshold minus one) that may result from individual storagedevice failures and/or network equipment failures without loss of dataand without the need for a redundant or backup copy. Further, the systemallows the data to be stored for an indefinite period of time withoutdata loss and does so in a secure manner (e.g., the system is veryresistant to attempts at hacking the data).

The second primary function (i.e., distributed data storage andretrieval) begins and ends with a user device 12-14. For instance, if asecond type of user device 14 has data 40 to store in the DSTN module22, it sends the data 40 to the DST processing unit 16 via its interface30. The interface 30 functions to mimic a conventional operating system(OS) file system interface (e.g., network file system (NFS), flash filesystem (FFS), disk file system (DFS), file transfer protocol (FTP),web-based distributed authoring and versioning (WebDAV), etc.) and/or ablock memory interface (e.g., small computer system interface (SCSI),internet small computer system interface (iSCSI), etc.). In addition,the interface 30 may attach a user identification code (ID) to the data40.

To support storage management, the DSTN managing unit 18 performs DSmanagement services. One such DS management service includes the DSTNmanaging unit 18 establishing distributed data storage parameters (e.g.,vault creation, distributed storage parameters, security parameters,billing information, user profile information, etc.) for a user device12-14 individually or as part of a group of user devices. For example,the DSTN managing unit 18 coordinates creation of a vault (e.g., avirtual memory block) within memory of the DSTN module 22 for a userdevice, a group of devices, or for public access and establishes pervault dispersed storage (DS) error encoding parameters for a vault. TheDSTN managing unit 18 may facilitate storage of DS error encodingparameters for each vault of a plurality of vaults by updating registryinformation for the distributed computing system 10. The facilitatingincludes storing updated registry information in one or more of the DSTNmodule 22, the user device 12, the DST processing unit 16, and the DSTintegrity processing unit 20.

The DS error encoding parameters (e.g., or dispersed storage errorcoding parameters) include data segmenting information (e.g., how manysegments data (e.g., a file, a group of files, a data block, etc.) isdivided into), segment security information (e.g., per segmentencryption, compression, integrity checksum, etc.), error codinginformation (e.g., pillar width, decode threshold, read threshold, writethreshold, etc.), slicing information (e.g., the number of encoded dataslices that will be created for each data segment); and slice securityinformation (e.g., per encoded data slice encryption, compression,integrity checksum, etc.).

The DSTN managing unit 18 creates and stores user profile information(e.g., an access control list (ACL)) in local memory and/or withinmemory of the DSTN module 22. The user profile information includesauthentication information, permissions, and/or the security parameters.The security parameters may include encryption/decryption scheme, one ormore encryption keys, key generation scheme, and/or dataencoding/decoding scheme.

The DSTN managing unit 18 creates billing information for a particularuser, a user group, a vault access, public vault access, etc. Forinstance, the DSTN managing unit 18 tracks the number of times a useraccesses a private vault and/or public vaults, which can be used togenerate a per-access billing information. In another instance, the DSTNmanaging unit 18 tracks the amount of data stored and/or retrieved by auser device and/or a user group, which can be used to generate aper-data-amount billing information.

Another DS management service includes the DSTN managing unit 18performing network operations, network administration, and/or networkmaintenance. Network operations includes authenticating user dataallocation requests (e.g., read and/or write requests), managingcreation of vaults, establishing authentication credentials for userdevices, adding/deleting components (e.g., user devices, DST executionunits, and/or DST processing units) from the distributed computingsystem 10, and/or establishing authentication credentials for DSTexecution units 36. Network administration includes monitoring devicesand/or units for failures, maintaining vault information, determiningdevice and/or unit activation status, determining device and/or unitloading, and/or determining any other system level operation thataffects the performance level of the system 10. Network maintenanceincludes facilitating replacing, upgrading, repairing, and/or expandinga device and/or unit of the system 10.

To support data storage integrity verification within the distributedcomputing system 10, the DST integrity processing unit 20 performsrebuilding of ‘bad’ or missing encoded data slices. At a high level, theDST integrity processing unit 20 performs rebuilding by periodicallyattempting to retrieve/list encoded data slices, and/or slice names ofthe encoded data slices, from the DSTN module 22. For retrieved encodedslices, they are checked for errors due to data corruption, outdatedversion, etc. If a slice includes an error, it is flagged as a ‘bad’slice. For encoded data slices that were not received and/or not listed,they are flagged as missing slices. Bad and/or missing slices aresubsequently rebuilt using other retrieved encoded data slices that aredeemed to be good slices to produce rebuilt slices. The rebuilt slicesare stored in memory of the DSTN module 22. Note that the DST integrityprocessing unit 20 may be a separate unit as shown, it may be includedin the DSTN module 22, it may be included in the DST processing unit 16,and/or distributed among the DST execution units 36.

To support distributed task processing on received data, the distributedcomputing system 10 has two primary operations: DST (distributed storageand/or task processing) management and DST execution on received data(an example of which will be discussed with reference to FIGS. 3-19).With respect to the storage portion of the DST management, the DSTNmanaging unit 18 functions as previously described. With respect to thetasking processing of the DST management, the DSTN managing unit 18performs distributed task processing (DTP) management services. One suchDTP management service includes the DSTN managing unit 18 establishingDTP parameters (e.g., user-vault affiliation information, billinginformation, user-task information, etc.) for a user device 12-14individually or as part of a group of user devices.

Another DTP management service includes the DSTN managing unit 18performing DTP network operations, network administration (which isessentially the same as described above), and/or network maintenance(which is essentially the same as described above). Network operationsinclude, but are not limited to, authenticating user task processingrequests (e.g., valid request, valid user, etc.), authenticating resultsand/or partial results, establishing DTP authentication credentials foruser devices, adding/deleting components (e.g., user devices, DSTexecution units, and/or DST processing units) from the distributedcomputing system, and/or establishing DTP authentication credentials forDST execution units.

To support distributed task processing on stored data, the distributedcomputing system 10 has two primary operations: DST (distributed storageand/or task) management and DST execution on stored data. With respectto the DST execution on stored data, if the second type of user device14 has a task request 38 for execution by the DSTN module 22, it sendsthe task request 38 to the DST processing unit 16 via its interface 30.An example of DST execution on stored data will be discussed in greaterdetail with reference to FIGS. 27-39. With respect to the DSTmanagement, it is substantially similar to the DST management to supportdistributed task processing on received data.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (TO)controller 56, a peripheral component interconnect (PCI) interface 58,an 10 interface module 60, at least one 10 device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSTN interface module 76.

The DSTN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). TheDSTN interface module 76 and/or the network interface module 70 mayfunction as the interface 30 of the user device 14 of FIG. 1. Furthernote that the IO device interface module 62 and/or the memory interfacemodules may be collectively or individually referred to as IO ports.

FIG. 3 is a diagram of an example of the distributed computing systemperforming a distributed storage and task processing operation. Thedistributed computing system includes a DST (distributed storage and/ortask) client module 34 (which may be in user device 14 and/or in DSTprocessing unit 16 of FIG. 1), a network 24, a plurality of DSTexecution units 1-n that includes two or more DST execution units 36 ofFIG. 1 (which form at least a portion of DSTN module 22 of FIG. 1), aDST managing module (not shown), and a DST integrity verification module(not shown). The DST client module 34 includes an outbound DSTprocessing section 80 and an inbound DST processing section 82. Each ofthe DST execution units 1-n includes a controller 86, a processingmodule 84, memory 88, a DT (distributed task) execution module 90, and aDST client module 34.

In an example of operation, the DST client module 34 receives data 92and one or more tasks 94 to be performed upon the data 92. The data 92may be of any size and of any content, where, due to the size (e.g.,greater than a few Terabytes), the content (e.g., secure data, etc.),and/or task(s) (e.g., MIPS intensive), distributed processing of thetask(s) on the data is desired. For example, the data 92 may be one ormore digital books, a copy of a company's emails, a large-scale Internetsearch, a video security file, one or more entertainment video files(e.g., television programs, movies, etc.), data files, and/or any otherlarge amount of data (e.g., greater than a few Terabytes).

Within the DST client module 34, the outbound DST processing section 80receives the data 92 and the task(s) 94. The outbound DST processingsection 80 processes the data 92 to produce slice groupings 96. As anexample of such processing, the outbound DST processing section 80partitions the data 92 into a plurality of data partitions. For eachdata partition, the outbound DST processing section 80 dispersed storage(DS) error encodes the data partition to produce encoded data slices andgroups the encoded data slices into a slice grouping 96. In addition,the outbound DST processing section 80 partitions the task 94 intopartial tasks 98, where the number of partial tasks 98 may correspond tothe number of slice groupings 96.

The outbound DST processing section 80 then sends, via the network 24,the slice groupings 96 and the partial tasks 98 to the DST executionunits 1-n of the DSTN module 22 of FIG. 1. For example, the outbound DSTprocessing section 80 sends slice group 1 and partial task 1 to DSTexecution unit 1. As another example, the outbound DST processingsection 80 sends slice group #n and partial task #n to DST executionunit #n.

Each DST execution unit performs its partial task 98 upon its slicegroup 96 to produce partial results 102. For example, DST execution unit#1 performs partial task #1 on slice group #1 to produce a partialresult #1, for results. As a more specific example, slice group #1corresponds to a data partition of a series of digital books and thepartial task #1 corresponds to searching for specific phrases, recordingwhere the phrase is found, and establishing a phrase count. In this morespecific example, the partial result #1 includes information as to wherethe phrase was found and includes the phrase count.

Upon completion of generating their respective partial results 102, theDST execution units send, via the network 24, their partial results 102to the inbound DST processing section 82 of the DST client module 34.The inbound DST processing section 82 processes the received partialresults 102 to produce a result 104. Continuing with the specificexample of the preceding paragraph, the inbound DST processing section82 combines the phrase count from each of the DST execution units 36 toproduce a total phrase count. In addition, the inbound DST processingsection 82 combines the ‘where the phrase was found’ information fromeach of the DST execution units 36 within their respective datapartitions to produce ‘where the phrase was found’ information for theseries of digital books.

In another example of operation, the DST client module 34 requestsretrieval of stored data within the memory of the DST execution units 36(e.g., memory of the DSTN module). In this example, the task 94 isretrieve data stored in the memory of the DSTN module. Accordingly, theoutbound DST processing section 80 converts the task 94 into a pluralityof partial tasks 98 and sends the partial tasks 98 to the respective DSTexecution units 1-n.

In response to the partial task 98 of retrieving stored data, a DSTexecution unit 36 identifies the corresponding encoded data slices 100and retrieves them. For example, DST execution unit #1 receives partialtask #1 and retrieves, in response thereto, retrieved slices #1. The DSTexecution units 36 send their respective retrieved slices 100 to theinbound DST processing section 82 via the network 24.

The inbound DST processing section 82 converts the retrieved slices 100into data 92. For example, the inbound DST processing section 82de-groups the retrieved slices 100 to produce encoded slices per datapartition. The inbound DST processing section 82 then DS error decodesthe encoded slices per data partition to produce data partitions. Theinbound DST processing section 82 de-partitions the data partitions torecapture the data 92.

FIG. 4 is a schematic block diagram of an embodiment of an outbounddistributed storage and/or task (DST) processing section 80 of a DSTclient module 34 FIG. 1 coupled to a DSTN module 22 of a FIG. 1 (e.g., aplurality of n DST execution units 36) via a network 24. The outboundDST processing section 80 includes a data partitioning module 110, adispersed storage (DS) error encoding module 112, a grouping selectormodule 114, a control module 116, and a distributed task control module118.

In an example of operation, the data partitioning module 110 partitionsdata 92 into a plurality of data partitions 120. The number ofpartitions and the size of the partitions may be selected by the controlmodule 116 via control 160 based on the data 92 (e.g., its size, itscontent, etc.), a corresponding task 94 to be performed (e.g., simple,complex, single step, multiple steps, etc.), DS encoding parameters(e.g., pillar width, decode threshold, write threshold, segment securityparameters, slice security parameters, etc.), capabilities of the DSTexecution units 36 (e.g., processing resources, availability ofprocessing recourses, etc.), and/or as may be inputted by a user, systemadministrator, or other operator (human or automated). For example, thedata partitioning module 110 partitions the data 92 (e.g., 100Terabytes) into 100,000 data segments, each being 1 Gigabyte in size.Alternatively, the data partitioning module 110 partitions the data 92into a plurality of data segments, where some of data segments are of adifferent size, are of the same size, or a combination thereof.

The DS error encoding module 112 receives the data partitions 120 in aserial manner, a parallel manner, and/or a combination thereof. For eachdata partition 120, the DS error encoding module 112 DS error encodesthe data partition 120 in accordance with control information 160 fromthe control module 116 to produce encoded data slices 122. The DS errorencoding includes segmenting the data partition into data segments,segment security processing (e.g., encryption, compression,watermarking, integrity check (e.g., CRC), etc.), error encoding,slicing, and/or per slice security processing (e.g., encryption,compression, watermarking, integrity check (e.g., CRC), etc.). Thecontrol information 160 indicates which steps of the DS error encodingare active for a given data partition and, for active steps, indicatesthe parameters for the step. For example, the control information 160indicates that the error encoding is active and includes error encodingparameters (e.g., pillar width, decode threshold, write threshold, readthreshold, type of error encoding, etc.).

The grouping selector module 114 groups the encoded slices 122 of a datapartition into a set of slice groupings 96. The number of slicegroupings corresponds to the number of DST execution units 36 identifiedfor a particular task 94. For example, if five DST execution units 36are identified for the particular task 94, the grouping selector modulegroups the encoded slices 122 of a data partition into five slicegroupings 96. The grouping selector module 114 outputs the slicegroupings 96 to the corresponding DST execution units 36 via the network24.

The distributed task control module 118 receives the task 94 andconverts the task 94 into a set of partial tasks 98. For example, thedistributed task control module 118 receives a task to find where in thedata (e.g., a series of books) a phrase occurs and a total count of thephrase usage in the data. In this example, the distributed task controlmodule 118 replicates the task 94 for each DST execution unit 36 toproduce the partial tasks 98. In another example, the distributed taskcontrol module 118 receives a task to find where in the data a firstphrase occurs, where in the data a second phrase occurs, and a totalcount for each phrase usage in the data. In this example, thedistributed task control module 118 generates a first set of partialtasks 98 for finding and counting the first phrase and a second set ofpartial tasks for finding and counting the second phrase. Thedistributed task control module 118 sends respective first and/or secondpartial tasks 98 to each DST execution unit 36.

FIG. 5 is a logic diagram of an example of a method for outbounddistributed storage and task (DST) processing that begins at step 126where a DST client module receives data and one or more correspondingtasks. The method continues at step 128 where the DST client moduledetermines a number of DST units to support the task for one or moredata partitions. For example, the DST client module may determine thenumber of DST units to support the task based on the size of the data,the requested task, the content of the data, a predetermined number(e.g., user indicated, system administrator determined, etc.), availableDST units, capability of the DST units, and/or any other factorregarding distributed task processing of the data. The DST client modulemay select the same DST units for each data partition, may selectdifferent DST units for the data partitions, or a combination thereof.

The method continues at step 130 where the DST client module determinesprocessing parameters of the data based on the number of DST unitsselected for distributed task processing. The processing parametersinclude data partitioning information, DS encoding parameters, and/orslice grouping information. The data partitioning information includes anumber of data partitions, size of each data partition, and/ororganization of the data partitions (e.g., number of data blocks in apartition, the size of the data blocks, and arrangement of the datablocks). The DS encoding parameters include segmenting information,segment security information, error encoding information (e.g.,dispersed storage error encoding function parameters including one ormore of pillar width, decode threshold, write threshold, read threshold,generator matrix), slicing information, and/or per slice securityinformation. The slice grouping information includes informationregarding how to arrange the encoded data slices into groups for theselected DST units. As a specific example, if the DST client moduledetermines that five DST units are needed to support the task, then itdetermines that the error encoding parameters include a pillar width offive and a decode threshold of three.

The method continues at step 132 where the DST client module determinestask partitioning information (e.g., how to partition the tasks) basedon the selected DST units and data processing parameters. The dataprocessing parameters include the processing parameters and DST unitcapability information. The DST unit capability information includes thenumber of DT (distributed task) execution units, execution capabilitiesof each DT execution unit (e.g., MIPS capabilities, processing resources(e.g., quantity and capability of microprocessors, CPUs, digital signalprocessors, co-processor, microcontrollers, arithmetic logic circuitry,and/or any other analog and/or digital processing circuitry),availability of the processing resources, memory information (e.g.,type, size, availability, etc.)), and/or any information germane toexecuting one or more tasks.

The method continues at step 134 where the DST client module processesthe data in accordance with the processing parameters to produce slicegroupings. The method continues at step 136 where the DST client modulepartitions the task based on the task partitioning information toproduce a set of partial tasks. The method continues at step 138 wherethe DST client module sends the slice groupings and the correspondingpartial tasks to respective DST units.

FIG. 6 is a schematic block diagram of an embodiment of the dispersedstorage (DS) error encoding module 112 of an outbound distributedstorage and task (DST) processing section. The DS error encoding module112 includes a segment processing module 142, a segment securityprocessing module 144, an error encoding module 146, a slicing module148, and a per slice security processing module 150. Each of thesemodules is coupled to a control module 116 to receive controlinformation 160 therefrom.

In an example of operation, the segment processing module 142 receives adata partition 120 from a data partitioning module and receivessegmenting information as the control information 160 from the controlmodule 116. The segmenting information indicates how the segmentprocessing module 142 is to segment the data partition 120. For example,the segmenting information indicates how many rows to segment the databased on a decode threshold of an error encoding scheme, indicates howmany columns to segment the data into based on a number and size of datablocks within the data partition 120, and indicates how many columns toinclude in a data segment 152. The segment processing module 142segments the data 120 into data segments 152 in accordance with thesegmenting information.

The segment security processing module 144, when enabled by the controlmodule 116, secures the data segments 152 based on segment securityinformation received as control information 160 from the control module116. The segment security information includes data compression,encryption, watermarking, integrity check (e.g., cyclic redundancy check(CRC), etc.), and/or any other type of digital security. For example,when the segment security processing module 144 is enabled, it maycompress a data segment 152, encrypt the compressed data segment, andgenerate a CRC value for the encrypted data segment to produce a securedata segment 154. When the segment security processing module 144 is notenabled, it passes the data segments 152 to the error encoding module146 or is bypassed such that the data segments 152 are provided to theerror encoding module 146.

The error encoding module 146 encodes the secure data segments 154 inaccordance with error correction encoding parameters received as controlinformation 160 from the control module 116. The error correctionencoding parameters (e.g., also referred to as dispersed storage errorcoding parameters) include identifying an error correction encodingscheme (e.g., forward error correction algorithm, a Reed-Solomon basedalgorithm, an online coding algorithm, an information dispersalalgorithm, etc.), a pillar width, a decode threshold, a read threshold,a write threshold, etc. For example, the error correction encodingparameters identify a specific error correction encoding scheme,specifies a pillar width of five, and specifies a decode threshold ofthree. From these parameters, the error encoding module 146 encodes adata segment 154 to produce an encoded data segment 156.

The slicing module 148 slices the encoded data segment 156 in accordancewith the pillar width of the error correction encoding parametersreceived as control information 160. For example, if the pillar width isfive, the slicing module 148 slices an encoded data segment 156 into aset of five encoded data slices. As such, for a plurality of encodeddata segments 156 for a given data partition, the slicing module outputsa plurality of sets of encoded data slices 158.

The per slice security processing module 150, when enabled by thecontrol module 116, secures each encoded data slice 158 based on slicesecurity information received as control information 160 from thecontrol module 116. The slice security information includes datacompression, encryption, watermarking, integrity check (e.g., CRC,etc.), and/or any other type of digital security. For example, when theper slice security processing module 150 is enabled, it compresses anencoded data slice 158, encrypts the compressed encoded data slice, andgenerates a CRC value for the encrypted encoded data slice to produce asecure encoded data slice 122. When the per slice security processingmodule 150 is not enabled, it passes the encoded data slices 158 or isbypassed such that the encoded data slices 158 are the output of the DSerror encoding module 112. Note that the control module 116 may beomitted and each module stores its own parameters.

FIG. 7 is a diagram of an example of a segment processing of a dispersedstorage (DS) error encoding module. In this example, a segmentprocessing module 142 receives a data partition 120 that includes 45data blocks (e.g., d1-d45), receives segmenting information (i.e.,control information 160) from a control module, and segments the datapartition 120 in accordance with the control information 160 to producedata segments 152. Each data block may be of the same size as other datablocks or of a different size. In addition, the size of each data blockmay be a few bytes to megabytes of data. As previously mentioned, thesegmenting information indicates how many rows to segment the datapartition into, indicates how many columns to segment the data partitioninto, and indicates how many columns to include in a data segment.

In this example, the decode threshold of the error encoding scheme isthree; as such the number of rows to divide the data partition into isthree. The number of columns for each row is set to 15, which is basedon the number and size of data blocks. The data blocks of the datapartition are arranged in rows and columns in a sequential order (i.e.,the first row includes the first 15 data blocks; the second row includesthe second 15 data blocks; and the third row includes the last 15 datablocks).

With the data blocks arranged into the desired sequential order, theyare divided into data segments based on the segmenting information. Inthis example, the data partition is divided into 8 data segments; thefirst 7 include 2 columns of three rows and the last includes 1 columnof three rows. Note that the first row of the 8 data segments is insequential order of the first 15 data blocks; the second row of the 8data segments in sequential order of the second 15 data blocks; and thethird row of the 8 data segments in sequential order of the last 15 datablocks. Note that the number of data blocks, the grouping of the datablocks into segments, and size of the data blocks may vary toaccommodate the desired distributed task processing function.

FIG. 8 is a diagram of an example of error encoding and slicingprocessing of the dispersed error encoding processing the data segmentsof FIG. 7. In this example, data segment 1 includes 3 rows with each rowbeing treated as one word for encoding. As such, data segment 1 includesthree words for encoding: word 1 including data blocks d1 and d2, word 2including data blocks d16 and d17, and word 3 including data blocks d31and d32. Each of data segments 2-7 includes three words where each wordincludes two data blocks. Data segment 8 includes three words where eachword includes a single data block (e.g., d15, d30, and d45).

In operation, an error encoding module 146 and a slicing module 148convert each data segment into a set of encoded data slices inaccordance with error correction encoding parameters as controlinformation 160. More specifically, when the error correction encodingparameters indicate a unity matrix Reed-Solomon based encodingalgorithm, 5 pillars, and decode threshold of 3, the first three encodeddata slices of the set of encoded data slices for a data segment aresubstantially similar to the corresponding word of the data segment. Forinstance, when the unity matrix Reed-Solomon based encoding algorithm isapplied to data segment 1, the content of the first encoded data slice(DS1_d1&2) of the first set of encoded data slices (e.g., correspondingto data segment 1) is substantially similar to content of the first word(e.g., d1 & d2); the content of the second encoded data slice(DS1_d16&17) of the first set of encoded data slices is substantiallysimilar to content of the second word (e.g., d16 & d17); and the contentof the third encoded data slice (DS1_d31&32) of the first set of encodeddata slices is substantially similar to content of the third word (e.g.,d31 & d32).

The content of the fourth and fifth encoded data slices (e.g., ES1_1 andES1_2) of the first set of encoded data slices include error correctiondata based on the first—third words of the first data segment. With suchan encoding and slicing scheme, retrieving any three of the five encodeddata slices allows the data segment to be accurately reconstructed.

The encoding and slicing of data segments 2-7 yield sets of encoded dataslices similar to the set of encoded data slices of data segment 1. Forinstance, the content of the first encoded data slice (DS2_d3&4) of thesecond set of encoded data slices (e.g., corresponding to data segment2) is substantially similar to content of the first word (e.g., d3 &d4); the content of the second encoded data slice (DS2_d18&19) of thesecond set of encoded data slices is substantially similar to content ofthe second word (e.g., d18 & d19); and the content of the third encodeddata slice (DS2_d33&34) of the second set of encoded data slices issubstantially similar to content of the third word (e.g., d33 & d34).The content of the fourth and fifth encoded data slices (e.g., ES1_1 andES1_2) of the second set of encoded data slices includes errorcorrection data based on the first-third words of the second datasegment.

FIG. 9 is a diagram of an example of grouping selection processing of anoutbound distributed storage and task (DST) processing in accordancewith group selection information as control information 160 from acontrol module. Encoded slices for data partition 122 are grouped inaccordance with the control information 160 to produce slice groupings96. In this example, a grouping selector module 114 organizes theencoded data slices into five slice groupings (e.g., one for each DSTexecution unit of a distributed storage and task network (DSTN) module).As a specific example, the grouping selector module 114 creates a firstslice grouping for a DST execution unit #1, which includes first encodedslices of each of the sets of encoded slices. As such, the first DSTexecution unit receives encoded data slices corresponding to data blocks1-15 (e.g., encoded data slices of contiguous data).

The grouping selector module 114 also creates a second slice groupingfor a DST execution unit #2, which includes second encoded slices ofeach of the sets of encoded slices. As such, the second DST executionunit receives encoded data slices corresponding to data blocks 16-30.The grouping selector module 114 further creates a third slice groupingfor DST execution unit #3, which includes third encoded slices of eachof the sets of encoded slices. As such, the third DST execution unitreceives encoded data slices corresponding to data blocks 31-45.

The grouping selector module 114 creates a fourth slice grouping for DSTexecution unit #4, which includes fourth encoded slices of each of thesets of encoded slices. As such, the fourth DST execution unit receivesencoded data slices corresponding to first error encoding information(e.g., encoded data slices of error coding (EC) data). The groupingselector module 114 further creates a fifth slice grouping for DSTexecution unit #5, which includes fifth encoded slices of each of thesets of encoded slices. As such, the fifth DST execution unit receivesencoded data slices corresponding to second error encoding information.

FIG. 10 is a diagram of an example of converting data 92 into slicegroups that expands on the preceding figures. As shown, the data 92 ispartitioned in accordance with a partitioning function 164 into aplurality of data partitions (1-x, where x is an integer greater than4). Each data partition (or chunkset of data) is encoded and groupedinto slice groupings as previously discussed by an encoding and groupingfunction 166. For a given data partition, the slice groupings are sentto distributed storage and task (DST) execution units. From datapartition to data partition, the ordering of the slice groupings to theDST execution units may vary.

For example, the slice groupings of data partition #1 is sent to the DSTexecution units such that the first DST execution receives first encodeddata slices of each of the sets of encoded data slices, whichcorresponds to a first continuous data chunk of the first data partition(e.g., refer to FIG. 9), a second DST execution receives second encodeddata slices of each of the sets of encoded data slices, whichcorresponds to a second continuous data chunk of the first datapartition, etc.

For the second data partition, the slice groupings may be sent to theDST execution units in a different order than it was done for the firstdata partition. For instance, the first slice grouping of the seconddata partition (e.g., slice group 2_1) is sent to the second DSTexecution unit; the second slice grouping of the second data partition(e.g., slice group 2_2) is sent to the third DST execution unit; thethird slice grouping of the second data partition (e.g., slice group2_3) is sent to the fourth DST execution unit; the fourth slice groupingof the second data partition (e.g., slice group 2_4, which includesfirst error coding information) is sent to the fifth DST execution unit;and the fifth slice grouping of the second data partition (e.g., slicegroup 2_5, which includes second error coding information) is sent tothe first DST execution unit.

The pattern of sending the slice groupings to the set of DST executionunits may vary in a predicted pattern, a random pattern, and/or acombination thereof from data partition to data partition. In addition,from data partition to data partition, the set of DST execution unitsmay change. For example, for the first data partition, DST executionunits 1-5 may be used; for the second data partition, DST executionunits 6-10 may be used; for the third data partition, DST executionunits 3-7 may be used; etc. As is also shown, the task is divided intopartial tasks that are sent to the DST execution units in conjunctionwith the slice groupings of the data partitions.

FIG. 11 is a schematic block diagram of an embodiment of a DST(distributed storage and/or task) execution unit that includes aninterface 169, a controller 86, memory 88, one or more DT (distributedtask) execution modules 90, and a DST client module 34. The memory 88 isof sufficient size to store a significant number of encoded data slices(e.g., thousands of slices to hundreds-of-millions of slices) and mayinclude one or more hard drives and/or one or more solid-state memorydevices (e.g., flash memory, DRAM, etc.).

In an example of storing a slice group, the DST execution modulereceives a slice grouping 96 (e.g., slice group #1) via interface 169.The slice grouping 96 includes, per partition, encoded data slices ofcontiguous data or encoded data slices of error coding (EC) data. Forslice group #1, the DST execution module receives encoded data slices ofcontiguous data for partitions #1 and #x (and potentially others between3 and x) and receives encoded data slices of EC data for partitions #2and #3 (and potentially others between 3 and x). Examples of encodeddata slices of contiguous data and encoded data slices of error coding(EC) data are discussed with reference to FIG. 9. The memory 88 storesthe encoded data slices of slice groupings 96 in accordance with memorycontrol information 174 it receives from the controller 86.

The controller 86 (e.g., a processing module, a CPU, etc.) generates thememory control information 174 based on a partial task(s) 98 anddistributed computing information (e.g., user information (e.g., userID, distributed computing permissions, data access permission, etc.),vault information (e.g., virtual memory assigned to user, user group,temporary storage for task processing, etc.), task validationinformation, etc.). For example, the controller 86 interprets thepartial task(s) 98 in light of the distributed computing information todetermine whether a requestor is authorized to perform the task 98, isauthorized to access the data, and/or is authorized to perform the taskon this particular data. When the requestor is authorized, thecontroller 86 determines, based on the task 98 and/or another input,whether the encoded data slices of the slice grouping 96 are to betemporarily stored or permanently stored. Based on the foregoing, thecontroller 86 generates the memory control information 174 to write theencoded data slices of the slice grouping 96 into the memory 88 and toindicate whether the slice grouping 96 is permanently stored ortemporarily stored.

With the slice grouping 96 stored in the memory 88, the controller 86facilitates execution of the partial task(s) 98. In an example, thecontroller 86 interprets the partial task 98 in light of thecapabilities of the DT execution module(s) 90. The capabilities includeone or more of MIPS capabilities, processing resources (e.g., quantityand capability of microprocessors, CPUs, digital signal processors,co-processor, microcontrollers, arithmetic logic circuitry, and/or anyother analog and/or digital processing circuitry), availability of theprocessing resources, etc. If the controller 86 determines that the DTexecution module(s) 90 have sufficient capabilities, it generates taskcontrol information 176.

The task control information 176 may be a generic instruction (e.g.,perform the task on the stored slice grouping) or a series ofoperational codes. In the former instance, the DT execution module 90includes a co-processor function specifically configured (fixed orprogrammed) to perform the desired task 98. In the latter instance, theDT execution module 90 includes a general processor topology where thecontroller stores an algorithm corresponding to the particular task 98.In this instance, the controller 86 provides the operational codes(e.g., assembly language, source code of a programming language, objectcode, etc.) of the algorithm to the DT execution module 90 forexecution.

Depending on the nature of the task 98, the DT execution module 90 maygenerate intermediate partial results 102 that are stored in the memory88 or in a cache memory (not shown) within the DT execution module 90.In either case, when the DT execution module 90 completes execution ofthe partial task 98, it outputs one or more partial results 102. Thepartial results 102 may also be stored in memory 88.

If, when the controller 86 is interpreting whether capabilities of theDT execution module(s) 90 can support the partial task 98, thecontroller 86 determines that the DT execution module(s) 90 cannotadequately support the task 98 (e.g., does not have the right resources,does not have sufficient available resources, available resources wouldbe too slow, etc.), it then determines whether the partial task 98should be fully offloaded or partially offloaded.

If the controller 86 determines that the partial task 98 should be fullyoffloaded, it generates DST control information 178 and provides it tothe DST client module 34. The DST control information 178 includes thepartial task 98, memory storage information regarding the slice grouping96, and distribution instructions. The distribution instructionsinstruct the DST client module 34 to divide the partial task 98 intosub-partial tasks 172, to divide the slice grouping 96 into sub-slicegroupings 170, and identify other DST execution units. The DST clientmodule 34 functions in a similar manner as the DST client module 34 ofFIGS. 3-10 to produce the sub-partial tasks 172 and the sub-slicegroupings 170 in accordance with the distribution instructions.

The DST client module 34 receives DST feedback 168 (e.g., sub-partialresults), via the interface 169, from the DST execution units to whichthe task was offloaded. The DST client module 34 provides thesub-partial results to the DST execution unit, which processes thesub-partial results to produce the partial result(s) 102.

If the controller 86 determines that the partial task 98 should bepartially offloaded, it determines what portion of the task 98 and/orslice grouping 96 should be processed locally and what should beoffloaded. For the portion that is being locally processed, thecontroller 86 generates task control information 176 as previouslydiscussed. For the portion that is being offloaded, the controller 86generates DST control information 178 as previously discussed.

When the DST client module 34 receives DST feedback 168 (e.g.,sub-partial results) from the DST executions units to which a portion ofthe task was offloaded, it provides the sub-partial results to the DTexecution module 90. The DT execution module 90 processes thesub-partial results with the sub-partial results it created to producethe partial result(s) 102.

The memory 88 may be further utilized to retrieve one or more of storedslices 100, stored results 104, partial results 102 when the DTexecution module 90 stores partial results 102 and/or results 104 in thememory 88. For example, when the partial task 98 includes a retrievalrequest, the controller 86 outputs the memory control 174 to the memory88 to facilitate retrieval of slices 100 and/or results 104.

FIG. 12 is a schematic block diagram of an example of operation of adistributed storage and task (DST) execution unit storing encoded dataslices and executing a task thereon. To store the encoded data slices ofa partition 1 of slice grouping 1, a controller 86 generates writecommands as memory control information 174 such that the encoded slicesare stored in desired locations (e.g., permanent or temporary) withinmemory 88.

Once the encoded slices are stored, the controller 86 provides taskcontrol information 176 to a distributed task (DT) execution module 90.As a first step of executing the task in accordance with the taskcontrol information 176, the DT execution module 90 retrieves theencoded slices from memory 88. The DT execution module 90 thenreconstructs contiguous data blocks of a data partition. As shown forthis example, reconstructed contiguous data blocks of data partition 1include data blocks 1-15 (e.g., d1-d15).

With the contiguous data blocks reconstructed, the DT execution module90 performs the task on the reconstructed contiguous data blocks. Forexample, the task may be to search the reconstructed contiguous datablocks for a particular word or phrase, identify where in thereconstructed contiguous data blocks the particular word or phraseoccurred, and/or count the occurrences of the particular word or phraseon the reconstructed contiguous data blocks. The DST execution unitcontinues in a similar manner for the encoded data slices of otherpartitions in slice grouping 1. Note that with using the unity matrixerror encoding scheme previously discussed, if the encoded data slicesof contiguous data are uncorrupted, the decoding of them is a relativelystraightforward process of extracting the data.

If, however, an encoded data slice of contiguous data is corrupted (ormissing), it can be rebuilt by accessing other DST execution units thatare storing the other encoded data slices of the set of encoded dataslices of the corrupted encoded data slice. In this instance, the DSTexecution unit having the corrupted encoded data slices retrieves atleast three encoded data slices (of contiguous data and of error codingdata) in the set from the other DST execution units (recall for thisexample, the pillar width is 5 and the decode threshold is 3). The DSTexecution unit decodes the retrieved data slices using the DS errorencoding parameters to recapture the corresponding data segment. The DSTexecution unit then re-encodes the data segment using the DS errorencoding parameters to rebuild the corrupted encoded data slice. Oncethe encoded data slice is rebuilt, the DST execution unit functions aspreviously described.

FIG. 13 is a schematic block diagram of an embodiment of an inbounddistributed storage and/or task (DST) processing section 82 of a DSTclient module coupled to DST execution units of a distributed storageand task network (DSTN) module via a network 24. The inbound DSTprocessing section 82 includes a de-grouping module 180, a DS (dispersedstorage) error decoding module 182, a data de-partitioning module 184, acontrol module 186, and a distributed task control module 188. Note thatthe control module 186 and/or the distributed task control module 188may be separate modules from corresponding ones of outbound DSTprocessing section or may be the same modules.

In an example of operation, the DST execution units have completedexecution of corresponding partial tasks on the corresponding slicegroupings to produce partial results 102. The inbound DST processingsection 82 receives the partial results 102 via the distributed taskcontrol module 188. The inbound DST processing section 82 then processesthe partial results 102 to produce a final result, or results 104. Forexample, if the task was to find a specific word or phrase within data,the partial results 102 indicate where in each of the prescribedportions of the data the corresponding DST execution units found thespecific word or phrase. The distributed task control module 188combines the individual partial results 102 for the correspondingportions of the data into a final result 104 for the data as a whole.

In another example of operation, the inbound DST processing section 82is retrieving stored data from the DST execution units (i.e., the DSTNmodule). In this example, the DST execution units output encoded dataslices 100 corresponding to the data retrieval requests. The de-groupingmodule 180 receives retrieved slices 100 and de-groups them to produceencoded data slices per data partition 122. The DS error decoding module182 decodes, in accordance with DS error encoding parameters, theencoded data slices per data partition 122 to produce data partitions120.

The data de-partitioning module 184 combines the data partitions 120into the data 92. The control module 186 controls the conversion ofretrieved slices 100 into the data 92 using control signals 190 to eachof the modules. For instance, the control module 186 providesde-grouping information to the de-grouping module 180, provides the DSerror encoding parameters to the DS error decoding module 182, andprovides de-partitioning information to the data de-partitioning module184.

FIG. 14 is a logic diagram of an example of a method that is executableby distributed storage and task (DST) client module regarding inboundDST processing. The method begins at step 194 where the DST clientmodule receives partial results. The method continues at step 196 wherethe DST client module retrieves the task corresponding to the partialresults. For example, the partial results include header informationthat identifies the requesting entity, which correlates to the requestedtask.

The method continues at step 198 where the DST client module determinesresult processing information based on the task. For example, if thetask were to identify a particular word or phrase within the data, theresult processing information would indicate to aggregate the partialresults for the corresponding portions of the data to produce the finalresult. As another example, if the task were to count the occurrences ofa particular word or phrase within the data, results of processing theinformation would indicate to add the partial results to produce thefinal results. The method continues at step 200 where the DST clientmodule processes the partial results in accordance with the resultprocessing information to produce the final result or results.

FIG. 15 is a diagram of an example of de-grouping selection processingof an inbound distributed storage and task (DST) processing section of aDST client module. In general, this is an inverse process of thegrouping module of the outbound DST processing section of FIG. 9.Accordingly, for each data partition (e.g., partition #1), thede-grouping module retrieves the corresponding slice grouping from theDST execution units (EU) (e.g., DST 1-5).

As shown, DST execution unit #1 provides a first slice grouping, whichincludes the first encoded slices of each of the sets of encoded slices(e.g., encoded data slices of contiguous data of data blocks 1-15); DSTexecution unit #2 provides a second slice grouping, which includes thesecond encoded slices of each of the sets of encoded slices (e.g.,encoded data slices of contiguous data of data blocks 16-30); DSTexecution unit #3 provides a third slice grouping, which includes thethird encoded slices of each of the sets of encoded slices (e.g.,encoded data slices of contiguous data of data blocks 31-45); DSTexecution unit #4 provides a fourth slice grouping, which includes thefourth encoded slices of each of the sets of encoded slices (e.g., firstencoded data slices of error coding (EC) data); and DST execution unit#5 provides a fifth slice grouping, which includes the fifth encodedslices of each of the sets of encoded slices (e.g., first encoded dataslices of error coding (EC) data).

The de-grouping module de-groups the slice groupings (e.g., receivedslices 100) using a de-grouping selector 180 controlled by a controlsignal 190 as shown in the example to produce a plurality of sets ofencoded data slices (e.g., retrieved slices for a partition into sets ofslices 122). Each set corresponding to a data segment of the datapartition.

FIG. 16 is a schematic block diagram of an embodiment of a dispersedstorage (DS) error decoding module 182 of an inbound distributed storageand task (DST) processing section. The DS error decoding module 182includes an inverse per slice security processing module 202, ade-slicing module 204, an error decoding module 206, an inverse segmentsecurity module 208, a de-segmenting processing module 210, and acontrol module 186.

In an example of operation, the inverse per slice security processingmodule 202, when enabled by the control module 186, unsecures eachencoded data slice 122 based on slice de-security information receivedas control information 190 (e.g., the compliment of the slice securityinformation discussed with reference to FIG. 6) received from thecontrol module 186. The slice security information includes datadecompression, decryption, de-watermarking, integrity check (e.g., CRCverification, etc.), and/or any other type of digital security. Forexample, when the inverse per slice security processing module 202 isenabled, it verifies integrity information (e.g., a CRC value) of eachencoded data slice 122, it decrypts each verified encoded data slice,and decompresses each decrypted encoded data slice to produce sliceencoded data 158. When the inverse per slice security processing module202 is not enabled, it passes the encoded data slices 122 as the slicedencoded data 158 or is bypassed such that the retrieved encoded dataslices 122 are provided as the sliced encoded data 158.

The de-slicing module 204 de-slices the sliced encoded data 158 intoencoded data segments 156 in accordance with a pillar width of the errorcorrection encoding parameters received as control information 190 fromthe control module 186. For example, if the pillar width is five, thede-slicing module 204 de-slices a set of five encoded data slices intoan encoded data segment 156. The error decoding module 206 decodes theencoded data segments 156 in accordance with error correction decodingparameters received as control information 190 from the control module186 to produce secure data segments 154. The error correction decodingparameters include identifying an error correction encoding scheme(e.g., forward error correction algorithm, a Reed-Solomon basedalgorithm, an information dispersal algorithm, etc.), a pillar width, adecode threshold, a read threshold, a write threshold, etc. For example,the error correction decoding parameters identify a specific errorcorrection encoding scheme, specify a pillar width of five, and specifya decode threshold of three.

The inverse segment security processing module 208, when enabled by thecontrol module 186, unsecures the secured data segments 154 based onsegment security information received as control information 190 fromthe control module 186. The segment security information includes datadecompression, decryption, de-watermarking, integrity check (e.g., CRC,etc.) verification, and/or any other type of digital security. Forexample, when the inverse segment security processing module 208 isenabled, it verifies integrity information (e.g., a CRC value) of eachsecure data segment 154, it decrypts each verified secured data segment,and decompresses each decrypted secure data segment to produce a datasegment 152. When the inverse segment security processing module 208 isnot enabled, it passes the decoded data segment 154 as the data segment152 or is bypassed.

The de-segment processing module 210 receives the data segments 152 andreceives de-segmenting information as control information 190 from thecontrol module 186. The de-segmenting information indicates how thede-segment processing module 210 is to de-segment the data segments 152into a data partition 120. For example, the de-segmenting informationindicates how the rows and columns of data segments are to be rearrangedto yield the data partition 120.

FIG. 17 is a diagram of an example of de-slicing and error decodingprocessing of a dispersed error decoding module. A de-slicing module 204receives at least a decode threshold number of encoded data slices 158for each data segment in accordance with control information 190 andprovides encoded data 156. In this example, a decode threshold is three.As such, each set of encoded data slices 158 is shown to have threeencoded data slices per data segment. The de-slicing module 204 mayreceive three encoded data slices per data segment because an associateddistributed storage and task (DST) client module requested retrievingonly three encoded data slices per segment or selected three of theretrieved encoded data slices per data segment. As shown, which is basedon the unity matrix encoding previously discussed with reference to FIG.8, an encoded data slice may be a data-based encoded data slice (e.g.,DS1_d1&d2) or an error code based encoded data slice (e.g., ES3_1).

An error decoding module 206 decodes the encoded data 156 of each datasegment in accordance with the error correction decoding parameters ofcontrol information 190 to produce secured segments 154. In thisexample, data segment 1 includes 3 rows with each row being treated asone word for encoding. As such, data segment 1 includes three words:word 1 including data blocks d1 and d2, word 2 including data blocks d16and d17, and word 3 including data blocks d31 and d32. Each of datasegments 2-7 includes three words where each word includes two datablocks. Data segment 8 includes three words where each word includes asingle data block (e.g., d15, d30, and d45).

FIG. 18 is a diagram of an example of de-segment processing of aninbound distributed storage and task (DST) processing. In this example,a de-segment processing module 210 receives data segments 152 (e.g.,1-8) and rearranges the data blocks of the data segments into rows andcolumns in accordance with de-segmenting information of controlinformation 190 to produce a data partition 120. Note that the number ofrows is based on the decode threshold (e.g., 3 in this specific example)and the number of columns is based on the number and size of the datablocks.

The de-segmenting module 210 converts the rows and columns of datablocks into the data partition 120. Note that each data block may be ofthe same size as other data blocks or of a different size. In addition,the size of each data block may be a few bytes to megabytes of data.

FIG. 19 is a diagram of an example of converting slice groups into data92 within an inbound distributed storage and task (DST) processingsection. As shown, the data 92 is reconstructed from a plurality of datapartitions (1-x, where x is an integer greater than 4). Each datapartition (or chunk set of data) is decoded and re-grouped using ade-grouping and decoding function 212 and a de-partition function 214from slice groupings as previously discussed. For a given datapartition, the slice groupings (e.g., at least a decode threshold perdata segment of encoded data slices) are received from DST executionunits. From data partition to data partition, the ordering of the slicegroupings received from the DST execution units may vary as discussedwith reference to FIG. 10.

FIG. 20 is a diagram of an example of a distributed storage and/orretrieval within the distributed computing system. The distributedcomputing system includes a plurality of distributed storage and/or task(DST) processing client modules 34 (one shown) coupled to a distributedstorage and/or task processing network (DSTN) module, or multiple DSTNmodules, via a network 24. The DST client module 34 includes an outboundDST processing section 80 and an inbound DST processing section 82. TheDSTN module includes a plurality of DST execution units. Each DSTexecution unit includes a controller 86, memory 88, one or moredistributed task (DT) execution modules 90, and a DST client module 34.

In an example of data storage, the DST client module 34 has data 92 thatit desires to store in the DSTN module. The data 92 may be a file (e.g.,video, audio, text, graphics, etc.), a data object, a data block, anupdate to a file, an update to a data block, etc. In this instance, theoutbound DST processing module 80 converts the data 92 into encoded dataslices 216 as will be further described with reference to FIGS. 21-23.The outbound DST processing module 80 sends, via the network 24, to theDST execution units for storage as further described with reference toFIG. 24.

In an example of data retrieval, the DST client module 34 issues aretrieve request to the DST execution units for the desired data 92. Theretrieve request may address each DST executions units storing encodeddata slices of the desired data, address a decode threshold number ofDST execution units, address a read threshold number of DST executionunits, or address some other number of DST execution units. In responseto the request, each addressed DST execution unit retrieves its encodeddata slices 100 of the desired data and sends them to the inbound DSTprocessing section 82, via the network 24.

When, for each data segment, the inbound DST processing section 82receives at least a decode threshold number of encoded data slices 100,it converts the encoded data slices 100 into a data segment. The inboundDST processing section 82 aggregates the data segments to produce theretrieved data 92.

FIG. 21 is a schematic block diagram of an embodiment of an outbounddistributed storage and/or task (DST) processing section 80 of a DSTclient module coupled to a distributed storage and task network (DSTN)module (e.g., a plurality of DST execution units) via a network 24. Theoutbound DST processing section 80 includes a data partitioning module110, a dispersed storage (DS) error encoding module 112, a groupingselector module 114, a control module 116, and a distributed taskcontrol module 118.

In an example of operation, the data partitioning module 110 isby-passed such that data 92 is provided directly to the DS errorencoding module 112. The control module 116 coordinates the by-passingof the data partitioning module 110 by outputting a bypass 220 messageto the data partitioning module 110.

The DS error encoding module 112 receives the data 92 in a serialmanner, a parallel manner, and/or a combination thereof. The DS errorencoding module 112 DS error encodes the data in accordance with controlinformation 160 from the control module 116 to produce encoded dataslices 218. The DS error encoding includes segmenting the data 92 intodata segments, segment security processing (e.g., encryption,compression, watermarking, integrity check (e.g., CRC, etc.)), errorencoding, slicing, and/or per slice security processing (e.g.,encryption, compression, watermarking, integrity check (e.g., CRC,etc.)). The control information 160 indicates which steps of the DSerror encoding are active for the data 92 and, for active steps,indicates the parameters for the step. For example, the controlinformation 160 indicates that the error encoding is active and includeserror encoding parameters (e.g., pillar width, decode threshold, writethreshold, read threshold, type of error encoding, etc.). The groupingselector module 114 groups the encoded slices 218 of the data segmentsinto pillars of slices 216. The number of pillars corresponds to thepillar width of the DS error encoding parameters. In this example, thedistributed task control module 118 facilitates the storage request.

FIG. 22 is a schematic block diagram of an example of a dispersedstorage (DS) error encoding module 112 for the example of FIG. 21. TheDS error encoding module 112 includes a segment processing module 142, asegment security processing module 144, an error encoding module 146, aslicing module 148, and a per slice security processing module 150. Eachof these modules is coupled to a control module 116 to receive controlinformation 160 therefrom.

In an example of operation, the segment processing module 142 receivesdata 92 and receives segmenting information as control information 160from the control module 116. The segmenting information indicates howthe segment processing module is to segment the data. For example, thesegmenting information indicates the size of each data segment. Thesegment processing module 142 segments the data 92 into data segments152 in accordance with the segmenting information.

The segment security processing module 144, when enabled by the controlmodule 116, secures the data segments 152 based on segment securityinformation received as control information 160 from the control module116. The segment security information includes data compression,encryption, watermarking, integrity check (e.g., CRC, etc.), and/or anyother type of digital security. For example, when the segment securityprocessing module 144 is enabled, it compresses a data segment 152,encrypts the compressed data segment, and generates a CRC value for theencrypted data segment to produce a secure data segment. When thesegment security processing module 144 is not enabled, it passes thedata segments 152 to the error encoding module 146 or is bypassed suchthat the data segments 152 are provided to the error encoding module146.

The error encoding module 146 encodes the secure data segments inaccordance with error correction encoding parameters received as controlinformation 160 from the control module 116. The error correctionencoding parameters include identifying an error correction encodingscheme (e.g., forward error correction algorithm, a Reed-Solomon basedalgorithm, an information dispersal algorithm, etc.), a pillar width, adecode threshold, a read threshold, a write threshold, etc. For example,the error correction encoding parameters identify a specific errorcorrection encoding scheme, specifies a pillar width of five, andspecifies a decode threshold of three. From these parameters, the errorencoding module 146 encodes a data segment to produce an encoded datasegment.

The slicing module 148 slices the encoded data segment in accordancewith a pillar width of the error correction encoding parameters. Forexample, if the pillar width is five, the slicing module slices anencoded data segment into a set of five encoded data slices. As such,for a plurality of data segments, the slicing module 148 outputs aplurality of sets of encoded data slices as shown within encoding andslicing function 222 as described.

The per slice security processing module 150, when enabled by thecontrol module 116, secures each encoded data slice based on slicesecurity information received as control information 160 from thecontrol module 116. The slice security information includes datacompression, encryption, watermarking, integrity check (e.g., CRC,etc.), and/or any other type of digital security. For example, when theper slice security processing module 150 is enabled, it may compress anencoded data slice, encrypt the compressed encoded data slice, andgenerate a CRC value for the encrypted encoded data slice to produce asecure encoded data slice tweaking. When the per slice securityprocessing module 150 is not enabled, it passes the encoded data slicesor is bypassed such that the encoded data slices 218 are the output ofthe DS error encoding module 112.

FIG. 23 is a diagram of an example of converting data 92 into pillarslice groups utilizing encoding, slicing and pillar grouping function224 for storage in memory of a distributed storage and task network(DSTN) module. As previously discussed the data 92 is encoded and slicedinto a plurality of sets of encoded data slices; one set per datasegment. The grouping selector module organizes the sets of encoded dataslices into pillars of data slices. In this example, the DS errorencoding parameters include a pillar width of 5 and a decode thresholdof 3. As such, for each data segment, 5 encoded data slices are created.

The grouping selector module takes the first encoded data slice of eachof the sets and forms a first pillar, which may be sent to the first DSTexecution unit. Similarly, the grouping selector module creates thesecond pillar from the second slices of the sets; the third pillar fromthe third slices of the sets; the fourth pillar from the fourth slicesof the sets; and the fifth pillar from the fifth slices of the set.

FIG. 24 is a schematic block diagram of an embodiment of a distributedstorage and/or task (DST) execution unit that includes an interface 169,a controller 86, memory 88, one or more distributed task (DT) executionmodules 90, and a DST client module 34. A computing core 26 may beutilized to implement the one or more DT execution modules 90 and theDST client module 34. The memory 88 is of sufficient size to store asignificant number of encoded data slices (e.g., thousands of slices tohundreds-of-millions of slices) and may include one or more hard drivesand/or one or more solid-state memory devices (e.g., flash memory, DRAM,etc.).

In an example of storing a pillar of slices 216, the DST execution unitreceives, via interface 169, a pillar of slices 216 (e.g., pillar #1slices). The memory 88 stores the encoded data slices 216 of the pillarof slices in accordance with memory control information 174 it receivesfrom the controller 86. The controller 86 (e.g., a processing module, aCPU, etc.) generates the memory control information 174 based ondistributed storage information (e.g., user information (e.g., user ID,distributed storage permissions, data access permission, etc.), vaultinformation (e.g., virtual memory assigned to user, user group, etc.),etc.). Similarly, when retrieving slices, the DST execution unitreceives, via interface 169, a slice retrieval request. The memory 88retrieves the slice in accordance with memory control information 174 itreceives from the controller 86. The memory 88 outputs the slice 100,via the interface 169, to a requesting entity.

FIG. 25 is a schematic block diagram of an example of operation of aninbound distributed storage and/or task (DST) processing section 82 forretrieving dispersed error encoded data 92. The inbound DST processingsection 82 includes a de-grouping module 180, a dispersed storage (DS)error decoding module 182, a data de-partitioning module 184, a controlmodule 186, and a distributed task control module 188. Note that thecontrol module 186 and/or the distributed task control module 188 may beseparate modules from corresponding ones of an outbound DST processingsection or may be the same modules.

In an example of operation, the inbound DST processing section 82 isretrieving stored data 92 from the DST execution units (i.e., the DSTNmodule). In this example, the DST execution units output encoded dataslices corresponding to data retrieval requests from the distributedtask control module 188. The de-grouping module 180 receives pillars ofslices 100 and de-groups them in accordance with control information 190from the control module 186 to produce sets of encoded data slices 218.The DS error decoding module 182 decodes, in accordance with the DSerror encoding parameters received as control information 190 from thecontrol module 186, each set of encoded data slices 218 to produce datasegments, which are aggregated into retrieved data 92. The datade-partitioning module 184 is by-passed in this operational mode via abypass signal 226 of control information 190 from the control module186.

FIG. 26 is a schematic block diagram of an embodiment of a dispersedstorage (DS) error decoding module 182 of an inbound distributed storageand task (DST) processing section. The DS error decoding module 182includes an inverse per slice security processing module 202, ade-slicing module 204, an error decoding module 206, an inverse segmentsecurity module 208, and a de-segmenting processing module 210. Thedispersed error decoding module 182 is operable to de-slice and decodeencoded slices per data segment 218 utilizing a de-slicing and decodingfunction 228 to produce a plurality of data segments that arede-segmented utilizing a de-segment function 230 to recover data 92.

In an example of operation, the inverse per slice security processingmodule 202, when enabled by the control module 186 via controlinformation 190, unsecures each encoded data slice 218 based on slicede-security information (e.g., the compliment of the slice securityinformation discussed with reference to FIG. 6) received as controlinformation 190 from the control module 186. The slice de-securityinformation includes data decompression, decryption, de-watermarking,integrity check (e.g., CRC verification, etc.), and/or any other type ofdigital security. For example, when the inverse per slice securityprocessing module 202 is enabled, it verifies integrity information(e.g., a CRC value) of each encoded data slice 218, it decrypts eachverified encoded data slice, and decompresses each decrypted encodeddata slice to produce slice encoded data. When the inverse per slicesecurity processing module 202 is not enabled, it passes the encodeddata slices 218 as the sliced encoded data or is bypassed such that theretrieved encoded data slices 218 are provided as the sliced encodeddata.

The de-slicing module 204 de-slices the sliced encoded data into encodeddata segments in accordance with a pillar width of the error correctionencoding parameters received as control information 190 from a controlmodule 186. For example, if the pillar width is five, the de-slicingmodule de-slices a set of five encoded data slices into an encoded datasegment. Alternatively, the encoded data segment may include just threeencoded data slices (e.g., when the decode threshold is 3).

The error decoding module 206 decodes the encoded data segments inaccordance with error correction decoding parameters received as controlinformation 190 from the control module 186 to produce secure datasegments. The error correction decoding parameters include identifyingan error correction encoding scheme (e.g., forward error correctionalgorithm, a Reed-Solomon based algorithm, an information dispersalalgorithm, etc.), a pillar width, a decode threshold, a read threshold,a write threshold, etc. For example, the error correction decodingparameters identify a specific error correction encoding scheme, specifya pillar width of five, and specify a decode threshold of three.

The inverse segment security processing module 208, when enabled by thecontrol module 186, unsecures the secured data segments based on segmentsecurity information received as control information 190 from thecontrol module 186. The segment security information includes datadecompression, decryption, de-watermarking, integrity check (e.g., CRC,etc.) verification, and/or any other type of digital security. Forexample, when the inverse segment security processing module is enabled,it verifies integrity information (e.g., a CRC value) of each securedata segment, it decrypts each verified secured data segment, anddecompresses each decrypted secure data segment to produce a datasegment 152. When the inverse segment security processing module 208 isnot enabled, it passes the decoded data segment 152 as the data segmentor is bypassed. The de-segmenting processing module 210 aggregates thedata segments 152 into the data 92 in accordance with controlinformation 190 from the control module 186.

FIG. 27 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module that includes aplurality of distributed storage and task (DST) execution units (#1through #n, where, for example, n is an integer greater than or equal tothree). Each of the DST execution units includes a DST client module 34,a controller 86, one or more DT (distributed task) execution modules 90,and memory 88.

In this example, the DSTN module stores, in the memory of the DSTexecution units, a plurality of DS (dispersed storage) encoded data(e.g., 1 through n, where n is an integer greater than or equal to two)and stores a plurality of DS encoded task codes (e.g., 1 through k,where k is an integer greater than or equal to two). The DS encoded datamay be encoded in accordance with one or more examples described withreference to FIGS. 3-19 (e.g., organized in slice groupings) or encodedin accordance with one or more examples described with reference toFIGS. 20-26 (e.g., organized in pillar groups). The data that is encodedinto the DS encoded data may be of any size and/or of any content. Forexample, the data may be one or more digital books, a copy of acompany's emails, a large-scale Internet search, a video security file,one or more entertainment video files (e.g., television programs,movies, etc.), data files, and/or any other large amount of data (e.g.,greater than a few Terabytes).

The tasks that are encoded into the DS encoded task code may be a simplefunction (e.g., a mathematical function, a logic function, an identifyfunction, a find function, a search engine function, a replace function,etc.), a complex function (e.g., compression, human and/or computerlanguage translation, text-to-voice conversion, voice-to-textconversion, etc.), multiple simple and/or complex functions, one or morealgorithms, one or more applications, etc. The tasks may be encoded intothe DS encoded task code in accordance with one or more examplesdescribed with reference to FIGS. 3-19 (e.g., organized in slicegroupings) or encoded in accordance with one or more examples describedwith reference to FIGS. 20-26 (e.g., organized in pillar groups).

In an example of operation, a DST client module of a user device or of aDST processing unit issues a DST request to the DSTN module. The DSTrequest may include a request to retrieve stored data, or a portionthereof, may include a request to store data that is included with theDST request, may include a request to perform one or more tasks onstored data, may include a request to perform one or more tasks on dataincluded with the DST request, etc. In the cases where the DST requestincludes a request to store data or to retrieve data, the client moduleand/or the DSTN module processes the request as previously discussedwith reference to one or more of FIGS. 3-19 (e.g., slice groupings)and/or 20-26 (e.g., pillar groupings). In the case where the DST requestincludes a request to perform one or more tasks on data included withthe DST request, the DST client module and/or the DSTN module processthe DST request as previously discussed with reference to one or more ofFIGS. 3-19.

In the case where the DST request includes a request to perform one ormore tasks on stored data, the DST client module and/or the DSTN moduleprocesses the DST request as will be described with reference to one ormore of FIGS. 28-39. In general, the DST client module identifies dataand one or more tasks for the DSTN module to execute upon the identifieddata. The DST request may be for a one-time execution of the task or foran on-going execution of the task. As an example of the latter, as acompany generates daily emails, the DST request may be to daily searchnew emails for inappropriate content and, if found, record the content,the email sender(s), the email recipient(s), email routing information,notify human resources of the identified email, etc.

FIG. 28 is a schematic block diagram of an example of a distributedcomputing system performing tasks on stored data. In this example, twodistributed storage and task (DST) client modules 1-2 are shown: thefirst may be associated with a user device and the second may beassociated with a DST processing unit or a high priority user device(e.g., high priority clearance user, system administrator, etc.). EachDST client module includes a list of stored data 234 and a list of taskscodes 236. The list of stored data 234 includes one or more entries ofdata identifying information, where each entry identifies data stored inthe DSTN module 22. The data identifying information (e.g., data ID)includes one or more of a data file name, a data file directory listing,DSTN addressing information of the data, a data object identifier, etc.The list of tasks 236 includes one or more entries of task codeidentifying information, when each entry identifies task codes stored inthe DSTN module 22. The task code identifying information (e.g., taskID) includes one or more of a task file name, a task file directorylisting, DSTN addressing information of the task, another type ofidentifier to identify the task, etc.

As shown, the list of data 234 and the list of tasks 236 are eachsmaller in number of entries for the first DST client module than thecorresponding lists of the second DST client module. This may occurbecause the user device associated with the first DST client module hasfewer privileges in the distributed computing system than the deviceassociated with the second DST client module. Alternatively, this mayoccur because the user device associated with the first DST clientmodule serves fewer users than the device associated with the second DSTclient module and is restricted by the distributed computing systemaccordingly. As yet another alternative, this may occur through norestraints by the distributed computing system, it just occurred becausethe operator of the user device associated with the first DST clientmodule has selected fewer data and/or fewer tasks than the operator ofthe device associated with the second DST client module.

In an example of operation, the first DST client module selects one ormore data entries 238 and one or more tasks 240 from its respectivelists (e.g., selected data ID and selected task ID). The first DSTclient module sends its selections to a task distribution module 232.The task distribution module 232 may be within a stand-alone device ofthe distributed computing system, may be within the user device thatcontains the first DST client module, or may be within the DSTN module22.

Regardless of the task distribution module's location, it generates DSTallocation information 242 from the selected task ID 240 and theselected data ID 238. The DST allocation information 242 includes datapartitioning information, task execution information, and/orintermediate result information. The task distribution module 232 sendsthe DST allocation information 242 to the DSTN module 22. Note that oneor more examples of the DST allocation information will be discussedwith reference to one or more of FIGS. 29-39.

The DSTN module 22 interprets the DST allocation information 242 toidentify the stored DS encoded data (e.g., DS error encoded data 2) andto identify the stored DS error encoded task code (e.g., DS errorencoded task code 1). In addition, the DSTN module 22 interprets the DSTallocation information 242 to determine how the data is to bepartitioned and how the task is to be partitioned. The DSTN module 22also determines whether the selected DS error encoded data 238 needs tobe converted from pillar grouping to slice grouping. If so, the DSTNmodule 22 converts the selected DS error encoded data into slicegroupings and stores the slice grouping DS error encoded data byoverwriting the pillar grouping DS error encoded data or by storing itin a different location in the memory of the DSTN module 22 (i.e., doesnot overwrite the pillar grouping DS encoded data).

The DSTN module 22 partitions the data and the task as indicated in theDST allocation information 242 and sends the portions to selected DSTexecution units of the DSTN module 22. Each of the selected DSTexecution units performs its partial task(s) on its slice groupings toproduce partial results. The DSTN module 22 collects the partial resultsfrom the selected DST execution units and provides them, as resultinformation 244, to the task distribution module. The result information244 may be the collected partial results, one or more final results asproduced by the DSTN module 22 from processing the partial results inaccordance with the DST allocation information 242, or one or moreintermediate results as produced by the DSTN module 22 from processingthe partial results in accordance with the DST allocation information242.

The task distribution module 232 receives the result information 244 andprovides one or more final results 104 therefrom to the first DST clientmodule. The final result(s) 104 may be result information 244 or aresult(s) of the task distribution module's processing of the resultinformation 244.

In concurrence with processing the selected task of the first DST clientmodule, the distributed computing system may process the selectedtask(s) of the second DST client module on the selected data(s) of thesecond DST client module. Alternatively, the distributed computingsystem may process the second DST client module's request subsequent to,or preceding, that of the first DST client module. Regardless of theordering and/or parallel processing of the DST client module requests,the second DST client module provides its selected data 238 and selectedtask 240 to a task distribution module 232. If the task distributionmodule 232 is a separate device of the distributed computing system orwithin the DSTN module, the task distribution modules 232 coupled to thefirst and second DST client modules may be the same module. The taskdistribution module 232 processes the request of the second DST clientmodule in a similar manner as it processed the request of the first DSTclient module.

FIG. 29 is a schematic block diagram of an embodiment of a taskdistribution module 232 facilitating the example of FIG. 28. The taskdistribution module 232 includes a plurality of tables it uses togenerate distributed storage and task (DST) allocation information 242for selected data and selected tasks received from a DST client module.The tables include data storage information 248, task storageinformation 250, distributed task (DT) execution module information 252,and task

sub-task mapping information 246.

The data storage information table 248 includes a data identification(ID) field 260, a data size field 262, an addressing information field264, distributed storage (DS) information 266, and may further includeother information regarding the data, how it is stored, and/or how itcan be processed. For example, DS encoded data #1 has a data ID of 1, adata size of AA (e.g., a byte size of a few Terabytes or more),addressing information of Addr_1_AA, and DS parameters of 3/5; SEG_1;and SLC_1. In this example, the addressing information may be a virtualaddress corresponding to the virtual address of the first storage word(e.g., one or more bytes) of the data and information on how tocalculate the other addresses, may be a range of virtual addresses forthe storage words of the data, physical addresses of the first storageword or the storage words of the data, may be a list of slice names ofthe encoded data slices of the data, etc. The DS parameters may includeidentity of an error encoding scheme, decode threshold/pillar width(e.g., 3/5 for the first data entry), segment security information(e.g., SEG_1), per slice security information (e.g., SLC_1), and/or anyother information regarding how the data was encoded into data slices.

The task storage information table 250 includes a task identification(ID) field 268, a task size field 270, an addressing information field272, distributed storage (DS) information 274, and may further includeother information regarding the task, how it is stored, and/or how itcan be used to process data. For example, DS encoded task #2 has a taskID of 2, a task size of XY, addressing information of Addr_2_XY, and DSparameters of 3/5; SEG_2; and SLC_2. In this example, the addressinginformation may be a virtual address corresponding to the virtualaddress of the first storage word (e.g., one or more bytes) of the taskand information on how to calculate the other addresses, may be a rangeof virtual addresses for the storage words of the task, physicaladdresses of the first storage word or the storage words of the task,may be a list of slices names of the encoded slices of the task code,etc. The DS parameters may include identity of an error encoding scheme,decode threshold/pillar width (e.g., 3/5 for the first data entry),segment security information (e.g., SEG_2), per slice securityinformation (e.g., SLC_2), and/or any other information regarding howthe task was encoded into encoded task slices. Note that the segmentand/or the per-slice security information include a type of encryption(if enabled), a type of compression (if enabled), watermarkinginformation (if enabled), and/or an integrity check scheme (if enabled).

The task

sub-task mapping information table 246 includes a task field 256 and asub-task field 258. The task field 256 identifies a task stored in thememory of a distributed storage and task network (DSTN) module and thecorresponding sub-task fields 258 indicates whether the task includessub-tasks and, if so, how many and if any of the sub-tasks are ordered.In this example, the task

sub-task mapping information table 246 includes an entry for each taskstored in memory of the DSTN module (e.g., task 1 through task k). Inparticular, this example indicates that task 1 includes 7 sub-tasks;task 2 does not include sub-tasks, and task k includes r number ofsub-tasks (where r is an integer greater than or equal to two).

The DT execution module table 252 includes a DST execution unit ID field276, a DT execution module ID field 278, and a DT execution modulecapabilities field 280. The DST execution unit ID field 276 includes theidentity of DST units in the DSTN module. The DT execution module IDfield 278 includes the identity of each DT execution unit in each DSTunit. For example, DST unit 1 includes three DT executions modules(e.g., 1_1, 1_2, and 1_3). The DT execution capabilities field 280includes identity of the capabilities of the corresponding DT executionunit. For example, DT execution module 1_1 includes capabilities X,where X includes one or more of MIPS capabilities, processing resources(e.g., quantity and capability of microprocessors, CPUs, digital signalprocessors, co-processor, microcontrollers, arithmetic logic circuitry,and/or any other analog and/or digital processing circuitry),availability of the processing resources, memory information (e.g.,type, size, availability, etc.), and/or any information germane toexecuting one or more tasks.

From these tables, the task distribution module 232 generates the DSTallocation information 242 to indicate where the data is stored, how topartition the data, where the task is stored, how to partition the task,which DT execution units should perform which partial task on which datapartitions, where and how intermediate results are to be stored, etc. Ifmultiple tasks are being performed on the same data or different data,the task distribution module factors such information into itsgeneration of the DST allocation information.

FIG. 30 is a diagram of a specific example of a distributed computingsystem performing tasks on stored data as a task flow 318. In thisexample, selected data 92 is data 2 and selected tasks are tasks 1, 2,and 3. Task 1 corresponds to analyzing translation of data from onelanguage to another (e.g., human language or computer language); task 2corresponds to finding specific words and/or phrases in the data; andtask 3 corresponds to finding specific translated words and/or phrasesin translated data.

In this example, task 1 includes 7 sub-tasks: task 1_1—identifynon-words (non-ordered); task 1_2—identify unique words (non-ordered);task 1_3—translate (non-ordered); task 1_4—translate back (ordered aftertask 1_3); task 1_5—compare to ID errors (ordered after task 1-4); task1_6—determine non-word translation errors (ordered after task 1_5 and1_1); and task 1_7—determine correct translations (ordered after 1_5 and1_2). The sub-task further indicates whether they are an ordered task(i.e., are dependent on the outcome of another task) or non-order (i.e.,are independent of the outcome of another task). Task 2 does not includesub-tasks and task 3 includes two sub-tasks: task 3_1 translate; andtask 3_2 find specific word or phrase in translated data.

In general, the three tasks collectively are selected to analyze datafor translation accuracies, translation errors, translation anomalies,occurrence of specific words or phrases in the data, and occurrence ofspecific words or phrases on the translated data. Graphically, the data92 is translated 306 into translated data 282; is analyzed for specificwords and/or phrases 300 to produce a list of specific words and/orphrases 286; is analyzed for non-words 302 (e.g., not in a referencedictionary) to produce a list of non-words 290; and is analyzed forunique words 316 included in the data 92 (i.e., how many different wordsare included in the data) to produce a list of unique words 298. Each ofthese tasks is independent of each other and can therefore be processedin parallel if desired.

The translated data 282 is analyzed (e.g., sub-task 3_2) for specifictranslated words and/or phrases 304 to produce a list of specifictranslated words and/or phrases 288. The translated data 282 istranslated back 308 (e.g., sub-task 1_4) into the language of theoriginal data to produce re-translated data 284. These two tasks aredependent on the translate task (e.g., task 1_3) and thus must beordered after the translation task, which may be in a pipelined orderingor a serial ordering. The re-translated data 284 is then compared 310with the original data 92 to find words and/or phrases that did nottranslate (one way and/or the other) properly to produce a list ofincorrectly translated words 294. As such, the comparing task (e.g.,sub-task 1_5) 310 is ordered after the translation 306 andre-translation tasks 308 (e.g., sub-tasks 1_3 and 1_4).

The list of words incorrectly translated 294 is compared 312 to the listof non-words 290 to identify words that were not properly translatedbecause the words are non-words to produce a list of errors due tonon-words 292. In addition, the list of words incorrectly translated 294is compared 314 to the list of unique words 298 to identify unique wordsthat were properly translated to produce a list of correctly translatedwords 296. The comparison may also identify unique words that were notproperly translated to produce a list of unique words that were notproperly translated. Note that each list of words (e.g., specific wordsand/or phrases, non-words, unique words, translated words and/orphrases, etc.,) may include the word and/or phrase, how many times it isused, where in the data it is used, and/or any other informationrequested regarding a word and/or phrase.

FIG. 31 is a schematic block diagram of an example of a distributedstorage and task processing network (DSTN) module storing data and taskcodes for the example of FIG. 30. As shown, DS encoded data 2 is storedas encoded data slices across the memory (e.g., stored in memories 88)of DST execution units 1-5; the DS encoded task code 1 (of task 1) andDS encoded task 3 are stored as encoded task slices across the memory ofDST execution units 1-5; and DS encoded task code 2 (of task 2) isstored as encoded task slices across the memory of DST execution units3-7. As indicated in the data storage information table and the taskstorage information table of FIG. 29, the respective data/task has DSparameters of 3/5 for their decode threshold/pillar width; hencespanning the memory of five DST execution units.

FIG. 32 is a diagram of an example of distributed storage and task (DST)allocation information 242 for the example of FIG. 30. The DSTallocation information 242 includes data partitioning information 320,task execution information 322, and intermediate result information 324.The data partitioning information 320 includes the data identifier (ID),the number of partitions to split the data into, address information foreach data partition, and whether the DS encoded data has to betransformed from pillar grouping to slice grouping. The task executioninformation 322 includes tabular information having a taskidentification field 326, a task ordering field 328, a data partitionfield ID 330, and a set of DT execution modules 332 to use for thedistributed task processing per data partition. The intermediate resultinformation 324 includes tabular information having a name ID field 334,an ID of the DST execution unit assigned to process the correspondingintermediate result 336, a scratch pad storage field 338, and anintermediate result storage field 340.

Continuing with the example of FIG. 30, where tasks 1-3 are to bedistributedly performed on data 2, the data partitioning informationincludes the ID of data 2. In addition, the task distribution moduledetermines whether the DS encoded data 2 is in the proper format fordistributed computing (e.g., was stored as slice groupings). If not, thetask distribution module indicates that the DS encoded data 2 formatneeds to be changed from the pillar grouping format to the slicegrouping format, which will be done by the DSTN module. In addition, thetask distribution module determines the number of partitions to dividethe data into (e.g., 2_1 through 2_z) and addressing information foreach partition.

The task distribution module generates an entry in the task executioninformation section for each sub-task to be performed. For example, task1_1 (e.g., identify non-words on the data) has no task ordering (i.e.,is independent of the results of other sub-tasks), is to be performed ondata partitions 2_1 through 2_z by DT execution modules 1_1, 2_1, 3_1,4_1, and 5_1. For instance, DT execution modules 1_1, 2_1, 3_1, 4_1, and5_1 search for non-words in data partitions 2_1 through 2_z to producetask 1_1 intermediate results (R1-1, which is a list of non-words). Task1_2 (e.g., identify unique words) has similar task execution informationas task 1_1 to produce task 1_2 intermediate results (R1-2, which is thelist of unique words).

Task 1_3 (e.g., translate) includes task execution information as beingnon-ordered (i.e., is independent), having DT execution modules 1_1,2_1, 3_1, 4_1, and 5_1 translate data partitions 2_1 through 2_4 andhaving DT execution modules 1_2, 2_2, 3_2, 4_2, and 5_2 translate datapartitions 2_5 through 2_z to produce task 1_3 intermediate results(R1-3, which is the translated data). In this example, the datapartitions are grouped, where different sets of DT execution modulesperform a distributed sub-task (or task) on each data partition group,which allows for further parallel processing.

Task 1_4 (e.g., translate back) is ordered after task 1_3 and is to beexecuted on task 1_3's intermediate result (e.g., R1-3_1) (e.g., thetranslated data). DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1 areallocated to translate back task 1_3 intermediate result partitionsR1-3_1 through R1-3_4 and DT execution modules 1_2, 2_2, 6_1, 7_1, and7_2 are allocated to translate back task 1_3 intermediate resultpartitions R1-3_5 through R1-3_z to produce task 1-4 intermediateresults (R1-4, which is the translated back data).

Task 1_5 (e.g., compare data and translated data to identify translationerrors) is ordered after task 1_4 and is to be executed on task 1_4'sintermediate results (R4-1) and on the data. DT execution modules 1_1,2_1, 3_1, 4_1, and 5_1 are allocated to compare the data partitions (2_1through 2_z) with partitions of task 1-4 intermediate results partitionsR1-4_1 through R1-4_z to produce task 1_5 intermediate results (R1-5,which is the list words translated incorrectly).

Task 1_6 (e.g., determine non-word translation errors) is ordered aftertasks 1_1 and 1_5 and is to be executed on tasks 1_1's and 1_5'sintermediate results (R1-1 and R1-5). DT execution modules 1_1, 2_1,3_1, 4_1, and 5_1 are allocated to compare the partitions of task 1_1intermediate results (R1-1_1 through R1-1_z) with partitions of task 1-5intermediate results partitions (R1-5_1 through R1-5_z) to produce task1_6 intermediate results (R1-6, which is the list translation errors dueto non-words).

Task 1_7 (e.g., determine words correctly translated) is ordered aftertasks 1_2 and 1_5 and is to be executed on tasks 1_2's and 1_5'sintermediate results (R1-1 and R1-5). DT execution modules 1_2, 2_2,3_2, 4_2, and 5_2 are allocated to compare the partitions of task 1_2intermediate results (R1-2_1 through R1-2_z) with partitions of task 1-5intermediate results partitions (R1-5_1 through R1-5_z) to produce task1_7 intermediate results (R1-7, which is the list of correctlytranslated words).

Task 2 (e.g., find specific words and/or phrases) has no task ordering(i.e., is independent of the results of other sub-tasks), is to beperformed on data partitions 2_1 through 2_z by DT execution modules3_1, 4_1, 5_1, 6_1, and 7_1. For instance, DT execution modules 3_1,4_1, 5_1, 6_1, and 7_1 search for specific words and/or phrases in datapartitions 2_1 through 2_z to produce task 2 intermediate results (R2,which is a list of specific words and/or phrases).

Task 3_2 (e.g., find specific translated words and/or phrases) isordered after task 1_3 (e.g., translate) is to be performed onpartitions R1-3_1 through R1-3_z by DT execution modules 1_2, 2_2, 3_2,4_2, and 5_2. For instance, DT execution modules 1_2, 2_2, 3_2, 4_2, and5_2 search for specific translated words and/or phrases in thepartitions of the translated data (R1-3_1 through R1-3_z) to producetask 3_2 intermediate results (R3-2, which is a list of specifictranslated words and/or phrases).

For each task, the intermediate result information indicates which DSTunit is responsible for overseeing execution of the task and, if needed,processing the partial results generated by the set of allocated DTexecution units. In addition, the intermediate result informationindicates a scratch pad memory for the task and where the correspondingintermediate results are to be stored. For example, for intermediateresult R1-1 (the intermediate result of task 1_1), DST unit 1 isresponsible for overseeing execution of the task 1_1 and coordinatesstorage of the intermediate result as encoded intermediate result slicesstored in memory of DST execution units 1-5. In general, the scratch padis for storing non-DS encoded intermediate results and the intermediateresult storage is for storing DS encoded intermediate results.

FIGS. 33-38 are schematic block diagrams of the distributed storage andtask network (DSTN) module performing the example of FIG. 30. In FIG.33, the DSTN module accesses the data 92 and partitions it into aplurality of partitions 1-z in accordance with distributed storage andtask network (DST) allocation information. For each data partition, theDSTN identifies a set of its DT (distributed task) execution modules 90to perform the task (e.g., identify non-words (i.e., not in a referencedictionary) within the data partition) in accordance with the DSTallocation information. From data partition to data partition, the setof DT execution modules 90 may be the same, different, or a combinationthereof (e.g., some data partitions use the same set while other datapartitions use different sets).

For the first data partition, the first set of DT execution modules(e.g., 1_1, 2_1, 3_1, 4_1, and 5_1 per the DST allocation information ofFIG. 32) executes task 1_1 to produce a first partial result 102 ofnon-words found in the first data partition. The second set of DTexecution modules (e.g., 1_1, 2_1, 3_1, 4_1, and 5_1 per the DSTallocation information of FIG. 32) executes task 1_1 to produce a secondpartial result 102 of non-words found in the second data partition. Thesets of DT execution modules (as per the DST allocation information)perform task 1_1 on the data partitions until the “z” set of DTexecution modules performs task 1_1 on the “zth” data partition toproduce a “zth” partial result 102 of non-words found in the “zth” datapartition.

As indicated in the DST allocation information of FIG. 32, DST executionunit 1 is assigned to process the first through “zth” partial results toproduce the first intermediate result (R1-1), which is a list ofnon-words found in the data. For instance, each set of DT executionmodules 90 stores its respective partial result in the scratchpad memoryof DST execution unit 1 (which is identified in the DST allocation ormay be determined by DST execution unit 1). A processing module of DSTexecution 1 is engaged to aggregate the first through “zth” partialresults to produce the first intermediate result (e.g., R1_1). Theprocessing module stores the first intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 1.

DST execution unit 1 engages its DST client module to slice groupingbased DS error encode the first intermediate result (e.g., the list ofnon-words). To begin the encoding, the DST client module determineswhether the list of non-words is of a sufficient size to partition(e.g., greater than a Terabyte). If yes, it partitions the firstintermediate result (R1-1) into a plurality of partitions (e.g., R1-1_1through R1-1_m). If the first intermediate result is not of sufficientsize to partition, it is not partitioned.

For each partition of the first intermediate result, or for the firstintermediate result, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes3/5 decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-5).

In FIG. 34, the DSTN module is performing task 1_2 (e.g., find uniquewords) on the data 92. To begin, the DSTN module accesses the data 92and partitions it into a plurality of partitions 1-z in accordance withthe DST allocation information or it may use the data partitions of task1_1 if the partitioning is the same. For each data partition, the DSTNidentifies a set of its DT execution modules to perform task 1_2 inaccordance with the DST allocation information. From data partition todata partition, the set of DT execution modules may be the same,different, or a combination thereof. For the data partitions, theallocated set of DT execution modules executes task 1_2 to produce apartial results (e.g., 1^(st) through “zth”) of unique words found inthe data partitions.

As indicated in the DST allocation information of FIG. 32, DST executionunit 1 is assigned to process the first through “zth” partial results102 of task 1_2 to produce the second intermediate result (R1-2), whichis a list of unique words found in the data 92. The processing module ofDST execution 1 is engaged to aggregate the first through “zth” partialresults of unique words to produce the second intermediate result. Theprocessing module stores the second intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 1.

DST execution unit 1 engages its DST client module to slice groupingbased DS error encode the second intermediate result (e.g., the list ofnon-words). To begin the encoding, the DST client module determineswhether the list of unique words is of a sufficient size to partition(e.g., greater than a Terabyte). If yes, it partitions the secondintermediate result (R1-2) into a plurality of partitions (e.g., R1-2_1through R1-2_m). If the second intermediate result is not of sufficientsize to partition, it is not partitioned.

For each partition of the second intermediate result, or for the secondintermediate results, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes3/5 decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-5).

In FIG. 35, the DSTN module is performing task 1_3 (e.g., translate) onthe data 92. To begin, the DSTN module accesses the data 92 andpartitions it into a plurality of partitions 1-z in accordance with theDST allocation information or it may use the data partitions of task 1_1if the partitioning is the same. For each data partition, the DSTNidentifies a set of its DT execution modules to perform task 1_3 inaccordance with the DST allocation information (e.g., DT executionmodules 1_1, 2_1, 3_1, 4_1, and 5_1 translate data partitions 2_1through 2_4 and DT execution modules 1_2, 2_2, 3_2, 4_2, and 5_2translate data partitions 2_5 through 2_z). For the data partitions, theallocated set of DT execution modules 90 executes task 1_3 to producepartial results 102 (e.g., 1^(st) through “zth”) of translated data.

As indicated in the DST allocation information of FIG. 32, DST executionunit 2 is assigned to process the first through “zth” partial results oftask 1_3 to produce the third intermediate result (R1-3), which istranslated data. The processing module of DST execution 2 is engaged toaggregate the first through “zth” partial results of translated data toproduce the third intermediate result. The processing module stores thethird intermediate result as non-DS error encoded data in the scratchpadmemory or in another section of memory of DST execution unit 2.

DST execution unit 2 engages its DST client module to slice groupingbased DS error encode the third intermediate result (e.g., translateddata). To begin the encoding, the DST client module partitions the thirdintermediate result (R1-3) into a plurality of partitions (e.g., R1-3_1through R1-3_y). For each partition of the third intermediate result,the DST client module uses the DS error encoding parameters of the data(e.g., DS parameters of data 2, which includes 3/5 decodethreshold/pillar width ratio) to produce slice groupings. The slicegroupings are stored in the intermediate result memory (e.g., allocatedmemory in the memories of DST execution units 2-6 per the DST allocationinformation).

As is further shown in FIG. 35, the DSTN module is performing task 1_4(e.g., retranslate) on the translated data of the third intermediateresult. To begin, the DSTN module accesses the translated data (from thescratchpad memory or from the intermediate result memory and decodes it)and partitions it into a plurality of partitions in accordance with theDST allocation information. For each partition of the third intermediateresult, the DSTN identifies a set of its DT execution modules 90 toperform task 1_4 in accordance with the DST allocation information(e.g., DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1 are allocated totranslate back partitions R1-3_1 through R1-3_4 and DT execution modules1_2, 2_2, 6_1, 7_1, and 7_2 are allocated to translate back partitionsR1-3_5 through R1-3_z). For the partitions, the allocated set of DTexecution modules executes task 1_4 to produce partial results 102(e.g., 1^(st) through “zth”) of re-translated data.

As indicated in the DST allocation information of FIG. 32, DST executionunit 3 is assigned to process the first through “zth” partial results oftask 1_4 to produce the fourth intermediate result (R1-4), which isretranslated data. The processing module of DST execution 3 is engagedto aggregate the first through “zth” partial results of retranslateddata to produce the fourth intermediate result. The processing modulestores the fourth intermediate result as non-DS error encoded data inthe scratchpad memory or in another section of memory of DST executionunit 3.

DST execution unit 3 engages its DST client module to slice groupingbased DS error encode the fourth intermediate result (e.g., retranslateddata). To begin the encoding, the DST client module partitions thefourth intermediate result (R1-4) into a plurality of partitions (e.g.,R1-4_1 through R1-4_z). For each partition of the fourth intermediateresult, the DST client module uses the DS error encoding parameters ofthe data (e.g., DS parameters of data 2, which includes 3/5 decodethreshold/pillar width ratio) to produce slice groupings. The slicegroupings are stored in the intermediate result memory (e.g., allocatedmemory in the memories of DST execution units 3-7 per the DST allocationinformation).

In FIG. 36, a distributed storage and task network (DSTN) module isperforming task 1_5 (e.g., compare) on data 92 and retranslated data ofFIG. 35. To begin, the DSTN module accesses the data 92 and partitionsit into a plurality of partitions in accordance with the DST allocationinformation or it may use the data partitions of task 1_1 if thepartitioning is the same. The DSTN module also accesses the retranslateddata from the scratchpad memory, or from the intermediate result memoryand decodes it, and partitions it into a plurality of partitions inaccordance with the DST allocation information. The number of partitionsof the retranslated data corresponds to the number of partitions of thedata.

For each pair of partitions (e.g., data partition 1 and retranslateddata partition 1), the DSTN identifies a set of its DT execution modules90 to perform task 1_5 in accordance with the DST allocation information(e.g., DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1). For each pairof partitions, the allocated set of DT execution modules executes task1_5 to produce partial results 102 (e.g., 1^(st) through “zth”) of alist of incorrectly translated words and/or phrases.

As indicated in the DST allocation information of FIG. 32, DST executionunit 1 is assigned to process the first through “zth” partial results oftask 1_5 to produce the fifth intermediate result (R1-5), which is thelist of incorrectly translated words and/or phrases. In particular, theprocessing module of DST execution 1 is engaged to aggregate the firstthrough “zth” partial results of the list of incorrectly translatedwords and/or phrases to produce the fifth intermediate result. Theprocessing module stores the fifth intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 1.

DST execution unit 1 engages its DST client module to slice groupingbased DS error encode the fifth intermediate result. To begin theencoding, the DST client module partitions the fifth intermediate result(R1-5) into a plurality of partitions (e.g., R1-5_1 through R1-5_z). Foreach partition of the fifth intermediate result, the DST client moduleuses the DS error encoding parameters of the data (e.g., DS parametersof data 2, which includes 3/5 decode threshold/pillar width ratio) toproduce slice groupings. The slice groupings are stored in theintermediate result memory (e.g., allocated memory in the memories ofDST execution units 1-5 per the DST allocation information).

As is further shown in FIG. 36, the DSTN module is performing task 1_6(e.g., translation errors due to non-words) on the list of incorrectlytranslated words and/or phrases (e.g., the fifth intermediate resultR1-5) and the list of non-words (e.g., the first intermediate resultR1-1). To begin, the DSTN module accesses the lists and partitions theminto a corresponding number of partitions.

For each pair of partitions (e.g., partition R1-1_1 and partitionR1-5_1), the DSTN identifies a set of its DT execution modules 90 toperform task 1_6 in accordance with the DST allocation information(e.g., DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1). For each pairof partitions, the allocated set of DT execution modules executes task1_6 to produce partial results 102 (e.g., 1^(st) through “zth”) of alist of incorrectly translated words and/or phrases due to non-words.

As indicated in the DST allocation information of FIG. 32, DST executionunit 2 is assigned to process the first through “zth” partial results oftask 1_6 to produce the sixth intermediate result (R1-6), which is thelist of incorrectly translated words and/or phrases due to non-words. Inparticular, the processing module of DST execution 2 is engaged toaggregate the first through “zth” partial results of the list ofincorrectly translated words and/or phrases due to non-words to producethe sixth intermediate result. The processing module stores the sixthintermediate result as non-DS error encoded data in the scratchpadmemory or in another section of memory of DST execution unit 2.

DST execution unit 2 engages its DST client module to slice groupingbased DS error encode the sixth intermediate result. To begin theencoding, the DST client module partitions the sixth intermediate result(R1-6) into a plurality of partitions (e.g., R1-6_1 through R1-6_z). Foreach partition of the sixth intermediate result, the DST client moduleuses the DS error encoding parameters of the data (e.g., DS parametersof data 2, which includes 3/5 decode threshold/pillar width ratio) toproduce slice groupings. The slice groupings are stored in theintermediate result memory (e.g., allocated memory in the memories ofDST execution units 2-6 per the DST allocation information).

As is still further shown in FIG. 36, the DSTN module is performing task1_7 (e.g., correctly translated words and/or phrases) on the list ofincorrectly translated words and/or phrases (e.g., the fifthintermediate result R1-5) and the list of unique words (e.g., the secondintermediate result R1-2). To begin, the DSTN module accesses the listsand partitions them into a corresponding number of partitions.

For each pair of partitions (e.g., partition R1-2_1 and partitionR1-5_1), the DSTN identifies a set of its DT execution modules 90 toperform task 1_7 in accordance with the DST allocation information(e.g., DT execution modules 1_2, 2_2, 3_2, 4_2, and 5_2). For each pairof partitions, the allocated set of DT execution modules executes task1_7 to produce partial results 102 (e.g., 1^(st) through “zth”) of alist of correctly translated words and/or phrases.

As indicated in the DST allocation information of FIG. 32, DST executionunit 3 is assigned to process the first through “zth” partial results oftask 1_7 to produce the seventh intermediate result (R1-7), which is thelist of correctly translated words and/or phrases. In particular, theprocessing module of DST execution 3 is engaged to aggregate the firstthrough “zth” partial results of the list of correctly translated wordsand/or phrases to produce the seventh intermediate result. Theprocessing module stores the seventh intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 3.

DST execution unit 3 engages its DST client module to slice groupingbased DS error encode the seventh intermediate result. To begin theencoding, the DST client module partitions the seventh intermediateresult (R1-7) into a plurality of partitions (e.g., R1-7_1 throughR1-7_z). For each partition of the seventh intermediate result, the DSTclient module uses the DS error encoding parameters of the data (e.g.,DS parameters of data 2, which includes 3/5 decode threshold/pillarwidth ratio) to produce slice groupings. The slice groupings are storedin the intermediate result memory (e.g., allocated memory in thememories of DST execution units 3-7 per the DST allocation information).

In FIG. 37, the distributed storage and task network (DSTN) module isperforming task 2 (e.g., find specific words and/or phrases) on the data92. To begin, the DSTN module accesses the data and partitions it into aplurality of partitions 1-z in accordance with the DST allocationinformation or it may use the data partitions of task 1_1 if thepartitioning is the same. For each data partition, the DSTN identifies aset of its DT execution modules 90 to perform task 2 in accordance withthe DST allocation information. From data partition to data partition,the set of DT execution modules may be the same, different, or acombination thereof. For the data partitions, the allocated set of DTexecution modules executes task 2 to produce partial results 102 (e.g.,1^(st) through “zth”) of specific words and/or phrases found in the datapartitions.

As indicated in the DST allocation information of FIG. 32, DST executionunit 7 is assigned to process the first through “zth” partial results oftask 2 to produce task 2 intermediate result (R2), which is a list ofspecific words and/or phrases found in the data. The processing moduleof DST execution 7 is engaged to aggregate the first through “zth”partial results of specific words and/or phrases to produce the task 2intermediate result. The processing module stores the task 2intermediate result as non-DS error encoded data in the scratchpadmemory or in another section of memory of DST execution unit 7.

DST execution unit 7 engages its DST client module to slice groupingbased DS error encode the task 2 intermediate result. To begin theencoding, the DST client module determines whether the list of specificwords and/or phrases is of a sufficient size to partition (e.g., greaterthan a Terabyte). If yes, it partitions the task 2 intermediate result(R2) into a plurality of partitions (e.g., R2_1 through R2_m). If thetask 2 intermediate result is not of sufficient size to partition, it isnot partitioned.

For each partition of the task 2 intermediate result, or for the task 2intermediate results, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes3/5 decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-4, and 7).

In FIG. 38, the distributed storage and task network (DSTN) module isperforming task 3 (e.g., find specific translated words and/or phrases)on the translated data (R1-3). To begin, the DSTN module accesses thetranslated data (from the scratchpad memory or from the intermediateresult memory and decodes it) and partitions it into a plurality ofpartitions in accordance with the DST allocation information. For eachpartition, the DSTN identifies a set of its DT execution modules toperform task 3 in accordance with the DST allocation information. Frompartition to partition, the set of DT execution modules may be the same,different, or a combination thereof. For the partitions, the allocatedset of DT execution modules 90 executes task 3 to produce partialresults 102 (e.g., 1^(st) through “zth”) of specific translated wordsand/or phrases found in the data partitions.

As indicated in the DST allocation information of FIG. 32, DST executionunit 5 is assigned to process the first through “zth” partial results oftask 3 to produce task 3 intermediate result (R3), which is a list ofspecific translated words and/or phrases found in the translated data.In particular, the processing module of DST execution 5 is engaged toaggregate the first through “zth” partial results of specific translatedwords and/or phrases to produce the task 3 intermediate result. Theprocessing module stores the task 3 intermediate result as non-DS errorencoded data in the scratchpad memory or in another section of memory ofDST execution unit 7.

DST execution unit 5 engages its DST client module to slice groupingbased DS error encode the task 3 intermediate result. To begin theencoding, the DST client module determines whether the list of specifictranslated words and/or phrases is of a sufficient size to partition(e.g., greater than a Terabyte). If yes, it partitions the task 3intermediate result (R3) into a plurality of partitions (e.g., R3_1through R3_m). If the task 3 intermediate result is not of sufficientsize to partition, it is not partitioned.

For each partition of the task 3 intermediate result, or for the task 3intermediate results, the DST client module uses the DS error encodingparameters of the data (e.g., DS parameters of data 2, which includes3/5 decode threshold/pillar width ratio) to produce slice groupings. Theslice groupings are stored in the intermediate result memory (e.g.,allocated memory in the memories of DST execution units 1-4, 5, and 7).

FIG. 39 is a diagram of an example of combining result information intofinal results 104 for the example of FIG. 30. In this example, theresult information includes the list of specific words and/or phrasesfound in the data (task 2 intermediate result), the list of specifictranslated words and/or phrases found in the data (task 3 intermediateresult), the list of non-words found in the data (task 1 firstintermediate result R1-1), the list of unique words found in the data(task 1 second intermediate result R1-2), the list of translation errorsdue to non-words (task 1 sixth intermediate result R1-6), and the listof correctly translated words and/or phrases (task 1 seventhintermediate result R1-7). The task distribution module provides theresult information to the requesting DST client module as the results104.

FIG. 40A is a schematic block diagram of an embodiment of a dispersedstorage network (DSN) 350, a replacement DSN 352, and a transitionstorage facility 354. The DSN 350 includes a plurality of distributedstorage and task (DST) processing units 1-D, at least one set of DSTexecution (EX) units 1-n, the network 24 of FIG. 1. The replacement DSN352 includes a plurality of DST processing units R1 through RD and atleast one set of DST execution units R1 through Rn. The transitionstorage facility 354 includes at least one of an external storagesystem, a local backup storage system, and yet another DSN. Each DSTprocessing unit includes the DST client module 34 of FIG. 1 and thememory 88 of FIG. 3. Each DST execution unit includes the processingmodule 84 of FIG. 3 and the memory 88 of FIG. 3. The DST processingunits may be implemented utilizing the DST processing unit 16 of FIG. 1.The DST execution units may be implemented utilizing the DST executionunit 36 of FIG. 1.

The DSN functions to transition a state of the DSN as the DSN storesdata in the set of DST execution units 1-n. The state of the DSNincludes one or more of a state of storage of temporary variablesassociated with processing of storage of data as operationalinformation, a state of storage of encoded data slices, and a state ofprocesses utilized to facilitate the storing of the data. A transitionof the state may include one or more of completing a process or taskassociated with the storing of the data, ending usage of the DSN, andactivating usage of the replacement DSN to continue to fulfill a need ofthe storing of the data.

In an example of operation of the transitioning of the state of the DSN,the transition storage facility 354 (e.g., or any other moduleassociated with the DSN or the replacement DSN) determines to initiatecapturing snapshot information from one or more units and/or modules ofthe DSN. The determining includes one or more of receiving a request,interpreting a DSN replacement schedule, detecting availability of thereplacement DSN, interpreting an error message, detecting that a DSNsystem health level is less than a minimum health threshold level, andreceiving a request. Having determined to initiate capturing snapshotinformation, the transition storage facility 354 issues snapshotscheduling information 356 to the one or more units and/or modules ofthe DSN. The issuing includes one or more of updating system registryinformation, publishing a message, issuing a schedule, issuing errormessages, and issuing a request.

At least some of the units and/or modules of the DSN receiving thesnapshot scheduling information 356 captures operational informationand/or encoded data slices as snapshot information. For example, theprocessing module 84 of the DST execution unit 1 pauses operation of oneor more processes, obtains the operational information from the memory88 of the DST execution unit 1, retrieves encoded data slices from thememory 88 of the DST execution unit 1, generates the snapshot schedulinginformation 356 to include the obtained operational information andretrieved encoded data slices, and resumes operations. Having capturedthe snapshot scheduling information 356, the at least some of the unitsand/or modules of the DSN sends the snapshot information 356 to thetransition storage facility 354 for temporary storage.

With the snapshot information 356 stored in the transition storagefacility, the transition storage facility 354 selects a storageoperations approach utilizing the temporarily stored snapshotinformation. The selecting includes one or more of detecting that thereplacement DSN 352 is available, interpreting an error message, andreceiving a request. The storage operations approach includes restartingstate of the DSN at the point of the snapshot, rolling back contents ofstored encoded data slices in the DSN to the time of the snapshot,utilizing the replacement DSN as a parallel storage mechanism, anddecommissioning the DSN after transitioning the state and/or slices tothe replacement DSN.

Having selected the storage operation approach, the transition storagefacility 354 initiates the selected storage operations approach. Forexample, the transition storage facility 354 sends the stored snapshotinformation 356 to the modules and units of the replacement DSN 352 forinitiation of operations as transition information 358 when the DSN 350is to be decommissioned and replaced by the replacement DSN 352. Themodules and/or units of the replacement DSN 352 initiate operation withthe operational parameters and/or encoded data slices of the transitioninformation 358.

FIG. 40B is a flowchart illustrating an example of transitioning a stateof a dispersed storage network. The method includes step 366 where aprocessing module (e.g., of a distributed storage and task (DST) clientmodule) determines to initiate capturing snapshot information from oneor more modules of a dispersed storage network (DSN). The determiningincludes at least one of receiving a request, interpreting a schedule,detecting availability of a replacement DSN, interpreting an errormessage, and detecting that a performance level of the DSN is less thana minimum performance threshold level.

The method continues at step 368 where the processing module issuessnapshot scheduling information to the one or more modules of the DSN.The issuing includes generating the snapshot scheduling information suchthat each module shall initiate pausing operations at substantially asame time frame (e.g., allowing for time to propagate information), andsending the snapshot scheduling information to the one or more modules.The issuing further includes one or more of the processing moduleupdating system registry information, publishing a message, issuing aschedule, issuing error messages, and issuing a request.

The method continues at step 370 where each module receiving thesnapshot scheduling information captures operational information and/orencoded data slices as the snapshot information. For example, the modulepauses operations of one or more processes associated with the module,obtains the operational information and/or encoded data slices inaccordance with the snapshot scheduling information, generates thesnapshot information, and may resume operations in accordance with thesnapshot scheduling information.

The method continues at step 372 where each module capturing thesnapshot information sends the snapshot information to a transitionstorage facility for temporary storage. For example, the moduleidentifies the transition storage facility (e.g., in accordance with thesnapshot scheduling information, by interpreting a query response, inaccordance with a predetermination, based on identifying a replacementDSN), and outputs the snapshot information to the identified transitionstorage facility.

The method continues at step 374 where the processing module selects astorage operations approach for utilizing of the temporarily storedsnapshot information. The selecting includes at least one of detectingthat the replacement DSN is available, interpreting an error message,and receiving a request. The method continues at step 376 where theprocessing module initiates a storage operations approach. For example,the processing module sends the snapshot information as transitioninformation to the replacement DSN when activating the replacement DSN.As another example, the processing module sends the snapshot informationto modules of the DSN when restarting the DSN in accordance with aprevious snapshot.

FIGS. 41A-B are schematic block diagrams of another embodiment of adispersed storage network (DSN) that includes a set of distributedstorage and task (DST) execution (EX) units 1-5, the network 24 of FIG.1, and the DST integrity processing unit 20 of FIG. 1. The set of DSTexecution units 1-5 includes a plurality of M sets of memories (e.g.,memory sets), where each DST execution unit includes a correspondingmemory of each set of the M sets of memories. For example, DST executionunit 2 includes memory 2-1 of the memory set 1, memory 2-2 of the memoryset 2, memory 2-3 of the memory set 3 etc. Each DST execution unit maybe implemented utilizing the DST execution unit 36 of FIG. 1.Alternatively, or in addition to, one or more DST execution units mayprovide functionality of the DST integrity processing unit 20 asdescribed in greater detail below. Hereafter, each DST execution unitmay be interchangeably referred to as a storage unit and the set of DSTexecution units may be interchangeably referred to as a set of storageunits.

The DSN functions to rebuild encoded data slices, where a data encodingentity (e.g., the DST processing unit 16 of FIG. 1) dispersed storageerror encodes data utilizing dispersed storage error encoding parametersto produce a plurality of sets of encoded data slices and stores theplurality of sets of encoded data slices in one or more of the memorysets, and where each set of encoded data slices includes a total numberof encoded data slices and a decode threshold number of encoded dataslices of each set of encoded data slices that is required to recoverthe data. From time to time, the DST integrity processing unit 20obtains slice availability information 1-5 from one or more memory setswith regards to one or more sets of encoded data slices and identifies aplurality of to-be rebuilt encoded data slices. The identifying includesone or more of interpreting list slice responses, interpreting readslice responses, receiving the slice availability information inresponse to a slice availability request, and interpreting an errormessage. For example, the DST integrity processing unit 20 identifiesmissing slices associated with a set of encoded data slices stored inthe memory set 3, where the missing slices were stored in the memories4-3 and 5-3 (e.g., memories 4-3 and 5-3 may be failing), and identifiesfurther missing slices associated with another set of encoded dataslices stored in the memory set 2, where the missing slices were storedin the memory 5-2 (e.g., memory 5-2 may be failing).

FIG. 41A illustrates steps of an example of operation of the rebuildingof encoded data slices, for each set of encoded data slices of aplurality of sets of encoded data slices that includes at least one ofthe plurality of to-be rebuilt encoded data slices, the DST integrityprocessing unit 20 determines a cumulative memory health for the memorydevices of the storage units storing other encoded data slices of therespective set of encoded data slices. For instance, the DST integrityprocessing unit 20 determines the cumulative memory health for thememory devices 1-2 through memory 4-2 of the memory set 2 when the atleast one of the plurality of to-be rebuilt encoded data slices isassociated with the memory 5-2 of the memory set 2 and determines thecumulative memory health for the memory devices 1-3 through memory 3-3of the memory set 3 when the another at least one of the plurality ofto-be rebuilt encoded data slices is associated with the memories 4-3and 5-3 of the memory set 3.

The determining the cumulative memory health includes, for each of thememory devices of the storage units storing the other encoded dataslices, determining whether the respective memory device is in a softfailure mode (e.g., failing to maintain storage of previously storedencoded data slices) or a non-failure mode (e.g., no-no storage errors).The determining may be based on one or more of the slice availabilityinformation, interpreting a memory test result, interpreting a failuretrend, and interpreting a read slice response. For example, the DSTintegrity processing unit 20 determines that the memory devices 1-2through 4-2 are in the non-failure mode and the memory 5-2 is in thesoft failure mode (e.g., some slice errors detected). As anotherexample, the DST integrity processing unit 20 determines that the memorydevices 1-3 through 3-3 are in the non-failure mode and the memories 4-3and 5-3 are in the soft failure mode (e.g., more slice errors detected).

The determining the cumulative of memory health further includes, foreach memory device in a soft failure mode, determining a soft failuremode level. For example, the DST integrity processing and 20 compares anumber of error bits to a low threshold level and a high thresholdlevel. For instance, the DST integrity processing unit 20 indicates ahigh soft failure mode level when the number of error bits is greaterthan 5 million error bits detected over one hour. In another instance,the DST integrity processing unit 20 indicates a low soft failure modelevel when the number of error bits is less than 5 million error bitsand greater than 10,000 error bits over one hour. Having determined thesoft failure mode level, the DST integrity processing unit 20 calculatesthe cumulative memory health based on a ratio of memory devices in thesoft failure mode to the memory devices in the non-failure mode andweights the ratio based on the soft failure modes levels. Havingcalculated the cumulative memory health, the DST integrity processingunit temporarily stores the cumulative memory health as memory failurerates for each of the memory sets 1-M.

Having determined the cumulative memory health, the DST integrityprocessing unit 20 determines a probability of data loss based on thecumulative memory health, respective dispersed storage error encodingparameters (e.g., may vary from sets of slices of one data object toanother), and a number of encoded data slices requiring rebuilding inthe respective set of encoded data slices. The determining theprobability of data loss includes determining a number corresponding tothe other encoded data slices (e.g., four other slices for the set ofencoded data slices associated with the memory set 2, three other slicesfor the set of encoded data slice associated with the memory set 3),determining a total number of encoded data slices in the respective setof encoded data slices from the dispersed storage error encodingparameters (e.g., 5), determining a decode threshold number of encodeddata slices of the respective set of encoded data slices from thedispersed storage error encoding parameters (e.g., 3), determining acurrent redundancy number of encoded data slices based on the totalnumber minus the decode threshold number and minus the number of encodeddata slices requiring rebuilding (e.g., 5−3−1=1 for the set of encodeddata slices associated with the memory set 2; 5−3−2=0 for the set ofencoded data slices associated with the memory set 3), and weighting thecurrent redundancy number based on the cumulative memory health toproduce the probability of data loss. For example, the DST integrityprocessing unit 20 calculates a higher probability of data loss for theset of encoded data slices associated with the memory set 2 as comparedto the set of encoded data slice associated with the memory set 3 eventhough more slices are to be rebuilt (e.g., slices 4, 5), for the set ofencoded data slice associated with the memory set 3, when the cumulativememory health of the memories 1-2 through 4-2 is less favorable than thecumulative memory health of the memories 1-3 through 3-3.

Having produced the probability of data loss, the DST integrityprocessing unit 20 prioritizes rebuilding of the plurality of to-berebuilt encoded data slices based on the probability of data loss foreach set of encoded data slices of the plurality of sets of encoded dataslices. For example, the DST integrity processing unit 20 prioritizesthe rebuilding of the to-be rebuilt encoded data slices associated withthe memory of 5-2 ahead of the rebuilding of the to-be rebuilt encodeddata slice associated with the memories 4-3 and 5-3 when the probabilityof data loss for the to-be rebuilt encoded data slice associated withthe memory 5-2 is greater than the probability of data loss for theto-be rebuilt encoded data slices associated with the memories 4-3 and5-3.

FIG. 41B illustrates further steps of the example of operation of therebuilding of encoded data slices where the DST integrity processingunit 20 rebuilds, in accordance with the prioritizing, a first to-berebuilt encoded data slice of the plurality of to-be rebuilt encodeddata slices to produce a first rebuilt encoded data slice. For example,the DST integrity processing unit 20 retrieves at least a decodethreshold number of encoded data slices from memories 1-2 through 4-2,where the encoded data slices are associated with the set of encodeddata slices of the highest priority slice to-be rebuilt associated withthe memory 5-2 (e.g., the first to-be rebuilt encoded data slice),dispersed storage error decodes the decode threshold number of retrievedslices to reproduce a data segment, and dispersed storage error encodesthe reproduced data segment to produce the first rebuilt encoded dataslice.

Alternatively, or in addition to, for a set of encoded data slices ofthe plurality of sets of encoded data slices having multiple encodeddata slices requiring rebuilding (e.g., the set of encoded data slicesassociated with the memory set 3), the DST integrity processing unit 20determines a first probability of data loss based on the cumulativememory health (e.g., with respect to an encoded data slice to-be rebuiltassociated with the memory 4-3 based where the first probability of dataloss is a function of memory health of the memory 5-3), the respectivedispersed storage error encoding parameters, and the multiple encodeddata slices requiring rebuilding in the respective set of encoded dataslices, and determines a second probability of data loss based on thecumulative memory health (e.g., with respect to an encoded data sliceto-be rebuilt associated with the memory 5-3 based where the secondprobability of data loss is a function of memory health of the memory4-3), the respective dispersed storage error encoding parameters, andthe multiple encoded data slices requiring rebuilding less one of themultiple encoded data slices requiring rebuilding. Having determined thefirst and second probabilities of data loss, the DST integrityprocessing unit 20 rebuilds the one of the multiple encoded data slicesrequiring rebuilding in accordance with the first probability of dataloss and rebuilds a second one of the multiple encoded data slicesrequiring rebuilding in accordance with the second probability of dataloss. For example, the DST integrity processing unit 20 rebuilds theencoded data slice to-be rebuilt associated with the memory 4-3 firstand rebuilds the encoded data slice to-be rebuilt associated with thememory 5-3 second.

Having produced the first rebuilt encoded data slice, the DST integrityprocessing unit 20 identifies a memory device for storing the firstrebuilt encoded data slice. The identifying may be based on one or moreof memory health of the memory device, a predetermination, a testresult, and a request. For example, the DST integrity processing unit 20identifies a new memory device with one of the storage units based on afavorable memory health of the new memory device for storing the firstrebuilt encoded data slice. As another example, the DST integrityprocessing unit 20 identifies an original memory when the memory healthof the original memory is favorable (e.g., when the memory health of thememory 5-2 becomes favorable). Having identified the memory device forstoring the first rebuilt encoded data slice, the DST integrityprocessing unit 20 sends the first rebuilt encoded data slice to theidentified memory. For example, the DST integrity processing unit 20sends, via the network 24, the prioritized rebuilt encoded data slice(e.g., the first rebuilt encoded data slice) for memory 5-2 to the DSTexecution unit 5 for storage in the memory 5-2 when the memory health isfavorable again of the memory 5-2. As another example, the DST integrityprocessing unit 20 sends, via the network 24, the first rebuilt encodeddata slice to the DST execution unit 5 for storage in the memory 5-1when the memory health is favorable for the memory 5-1 and unfavorablefor the memory 5-2.

Further alternatively, or in addition to, the DST integrity processingunit 20 identifies new to-be rebuilt encoded data slices. Havingidentified the new to-be rebuilt encoded data slices, the DST integrityprocessing unit 20 updates the plurality of sets of encoded data slicesto include each new set of encoded data slices including at least one ofthe new to-be rebuilt encoded data slices and to exclude sets of theplurality of sets of encoded data slices for which the at least one ofthe plurality of to-be rebuilt encoded data slices has been rebuilt toproduce an updated plurality of sets of encoded data slices.

Having updated the plurality of sets of encoded data slices, for eachset of encoded data slices of the updated plurality of sets of encodeddata slices, the DST integrity processing unit 20 determines a newcumulative memory health for memory devices of storage units storingother encoded data slices of the respective set of encoded data slices,determines a new probability of data loss based on the new cumulativememory health, the respective dispersed storage error encodingparameters, and the number of encoded data slices requiring rebuildingin the respective set of encoded data slices, updates prioritizingrebuilding of remaining to-be rebuilt encoded data slices of theplurality of to-be rebuilt encoded data slices and of the new to-berebuilt encoded data slices based on the new probability of data lossfor each set of encoded data slices of the updated plurality of sets ofencoded data slices, and rebuilds, in accordance with the updatedprioritizing, a second to-be rebuilt encoded data slice of the remainingto-be rebuilt encoded data slices of the plurality of to-be rebuiltencoded data slices and of the new to-be rebuilt encoded data slices.

FIG. 41C is a flowchart illustrating an example of rebuilding encodeddata slices. In particular, a method to rebuild a plurality of to-berebuilt encoded data slices in a dispersed storage network (DSN) ispresented for use in conjunction with one or more functions and featuresdescribed in conjunction with FIGS. 1-39, 41A-B, and also FIG. 41C. Themethod includes step 400 where a processing module of a computing deviceof one or more computing devices of a dispersed storage network (DSN),for each set of encoded data slices of a plurality of sets of encodeddata slices that includes at least one of the plurality of to-be rebuiltencoded data slices, determines a cumulative memory health for memorydevices of storage units storing other encoded data slices of therespective set of encoded data slices. The determining the cumulativememory health includes, for each of the memory devices of the storageunits storing the other encoded data slices, determining whether therespective memory device is in a soft failure mode or a non-failuremode, for each memory device in a soft failure mode, determining a softfailure mode level, and calculating the cumulative memory health basedon a ratio of memory devices in the soft failure mode to the memorydevices in the non-failure mode and weighting the ratio based on thesoft failure modes levels.

The method continues at step 402 where the processing module determinesa probability of data loss based on the cumulative memory health,respective dispersed storage error encoding parameters, and a number ofencoded data slices requiring rebuilding in the respective set ofencoded data slices. The determining the probability of data lossincludes determining a number corresponding to the other encoded dataslices, determining a total number of encoded data slices in therespective set of encoded data slices from the dispersed storage errorencoding parameters, determining a decode threshold number of encodeddata slices of the respective set of encoded data slices from thedispersed storage error encoding parameters, determining a currentredundancy number of encoded data slices based on the total number minusthe decode threshold number and minus the number of encoded data slicesrequiring rebuilding, and weighting the current redundancy number basedon the cumulative memory health to produce the probability of data loss.

The method continues at step 404 where the processing module prioritizesrebuilding of the plurality of to-be rebuilt encoded data slices basedon the probability of data loss for each set of encoded data slices ofthe plurality of sets of encoded data slices. The method continues atstep 406 where the processing module rebuilds, in accordance with theprioritizing, a first to-be rebuilt encoded data slice of the pluralityof to-be rebuilt encoded data slices to produce a first rebuilt encodeddata slice. Alternatively, or in addition to, for a set of encoded dataslices of the plurality of sets of encoded data slices having multipleencoded data slices requiring rebuilding, the processing moduledetermines a first probability of data loss based on the cumulativememory health, the respective dispersed storage error encodingparameters, and the multiple encoded data slices requiring rebuilding inthe respective set of encoded data slices, determines a secondprobability of data loss based on the cumulative memory health, therespective dispersed storage error encoding parameters, and the multipleencoded data slices requiring rebuilding less one of the multipleencoded data slices requiring rebuilding, rebuilds the one of themultiple encoded data slices requiring rebuilding in accordance with thefirst probability of data loss, and rebuilds a second one of themultiple encoded data slices requiring rebuilding in accordance with thesecond probability of data loss.

The method continues at step 408 where the processing module identifiesa new memory device with one of the storage units based on a favorablememory health of the new memory device for storing the first rebuiltencoded data slice. The method continues at step 410 where theprocessing module identifies new to-be rebuilt encoded data slices(e.g., interprets list slice responses, interprets a storage errormessage). The method continues at step 412 where the processing moduleupdates the plurality of sets of encoded data slices to include each newset of encoded data slices including at least one of the new to-berebuilt encoded data slices and to exclude sets of the plurality of setsof encoded data slices for which the at least one of the plurality ofto-be rebuilt encoded data slices has been rebuilt to produce an updatedplurality of sets of encoded data slices.

For each set of encoded data slices of the updated plurality of sets ofencoded data slices, the method continues at step 414 where theprocessing module determines a new cumulative memory health for memorydevices of storage units storing other encoded data slices of therespective set of encoded data slices. The method continues at step 416where the processing module determines a new probability of data lossbased on the new cumulative memory health, the respective dispersedstorage error encoding parameters, and the number of encoded data slicesrequiring rebuilding in the respective set of encoded data slices.

The method continues at step 418 where the processing module updatesprioritizing rebuilding of remaining to-be rebuilt encoded data slicesof the plurality of to-be rebuilt encoded data slices and of the newto-be rebuilt encoded data slices based on the new probability of dataloss for each set of encoded data slices of the updated plurality ofsets of encoded data slices. The method continues at step 420 where theprocessing module rebuilds, in accordance with the updated prioritizing,a second to-be rebuilt encoded data slice of the remaining to-be rebuiltencoded data slices of the plurality of to-be rebuilt encoded dataslices and of the new to-be rebuilt encoded data slices.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the dispersed storagenetwork or by other devices. In addition, at least one memory section(e.g., a non-transitory computer readable storage medium) that storesoperational instructions can, when executed by one or more processingmodules of one or more computing devices (e.g., a group of computingdevices) of the dispersed storage network (DSN), cause the one or morecomputing devices to perform any or all of the method steps describedabove.

FIG. 42A is a schematic block diagram of another embodiment of adispersed storage network (DSN) that includes the distributed storageand task (DST) processing unit 16 of FIG. 1, the network 24 of FIG. 1,and a set of DST execution (EX) units 1-n. The DST processing unit 16includes the DST client module 34 of FIG. 1. Each DST execution unit maybe implemented utilizing the DST execution unit 36 of FIG. 1.

The DSN functions to select retrieval locations for recovering data fromthe set of DST execution units 1-n. In an example of operation, the DSTclient module 34 determines to recover a data segment from the set ofDST execution units, where the data segment was dispersed storage errorencoded to produce a set of encoded data slices, and where the set ofencoded data slices are stored in the set of DST execution units. Thedetermining includes at least one of receiving a read data request 430,receiving a rebuilding request, identifying the data segment based on adata object identifier, and identifying slice names based on theidentity of the data segment.

Having determined to recover the data segment, the DST client module 34identifies retrieval locations of the set of encoded data slices. Theidentifying includes at least one of interpreting an entry of adispersed hierarchical index, performing a DSN directory lookup, andaccessing a slice location table utilizing the identified slice names(e.g., identify the set of DST execution units as the storagelocations).

Having identified the retrieval locations, for each retrieval location,the DST client module 34 obtains performance information. The obtainingincludes at least one of interpreting a query response, initiating atest, interpreting a test result, and performing a lookup. The DSNperformance information includes one or more of a loading level,retrieval latency of a DST execution unit, and a network bandwidthcapacity level.

Having obtained the performance information, for each k+x number ofcandidate retrieval locations of the set of DST execution units forpotential utilization, where k+x ranges from k+1 to n−1 (e.g., k=adecode threshold number of an information dispersal algorithm (IDA),n=IDA width), determine a cost-benefit level for each permutation ofcorresponding DST execution units. The determining includes at least oneof estimating a network loading impact level, and estimating a decodelatency level (e.g., latency to obtain a decode threshold number ofencoded data slices and decode them to reproduce the data segment).

Having determined the cost-benefit levels, the DST client module 34selects a permutation of the plurality of permutations based on thecorresponding plurality of cost-benefit levels. The selecting includesat least one of identifying a permutation with a most favorablecost-benefit level as the selected permutation and randomly selecting apermutation with a cost-benefit level that is greater than a minimumcost-benefit threshold level.

Having selected the permutation, the DST client module 34 issues readslice requests 432 to the k+x number of retrieval locationscorresponding to the selected permutation. For example, the DST clientmodule 34 identifies corresponding DST execution units, generates thek+x number of read slice requests, sends, via the network 24, the readslice requests 432 to the identified corresponding DST execution units.

Having issued the read slice requests 432, when receiving a decodethreshold number of encoded data slices within a response timeframe(e.g., receiving read slice responses 434), the DST client module 34dispersed storage error decodes the received decode threshold number ofencoded data slices to reproduce the data segment for inclusion in aread data response 436. When not receiving the decode threshold numberof encoded data slices within a response timeframe, the DST clientmodule 34 issues at least one more read slice request 432 to anadditional retrieval location in accordance with the selectedpermutation and a most favorable cost-benefit level in accordance withfavorable retrieval locations and available additional retrievallocations. For example, the DST client module 34 selects the additionalretrieval locations to maximize the cost-benefit level, where anunfavorable retrieval location has been excluded.

FIG. 42B is a flowchart illustrating an example of selecting retrievallocations. The method includes step 444 where a processing module (e.g.,of a distributed storage and task (DST) client module) determines torecover a data segment from a set of storage units. The determiningincludes at least one of receiving a read data request and identifying aset of encoded data slices associated with the data segment, where thedata segment was dispersed storage error encoded to produce the set ofencoded data slices for storage in the set of storage units.

The method continues at step 446 where the processing module identifiescandidate retrieval locations of the set of storage units. For example,the processing module interprets encoded data slice location informationbased on the set of slice names to produce a storage unit identifier foreach retrieval location. For each retrieval location, the methodcontinues at step 448 where the processing module obtains performanceinformation. The obtaining includes at least one of interpreting a testresult and accessing a historical performance record.

The method continues at step 450 where the processing module determinesa cost-benefit level for each permutation of a selected number ofstorage locations of the candidate retrieval locations. The determiningincludes at least one of identifying permutations, and for eachpermutation, estimating incremental network loading level, andestimating resulting recovery latency.

The method continues at step 452 where the processing module selects apermutation based on the cost-benefit level for each permutation. Theselecting includes at least one of identifying a permutation associatedwith a most favorable cost-benefit level and randomly selecting apermutation of the plurality of permutations associated with acost-benefit level that is greater than a minimum cost-benefit thresholdlevel.

The method continues at step 454 where the processing module initiatesretrieval of encoded data slices from the corresponding retrievallocations of the selected permutation. For example, the processingmodule issues read slice requests to storage units of retrievallocations associated with the selected permutation and receives readslice responses that includes encoded data slices.

The method continues at step 456 where the processing module reproducesthe data segment when receiving a decode threshold number of encodeddata slices. For example, the processing module receives the decodethreshold number of encoded data slices and dispersed storage errordecodes the decode threshold number of encoded data slices to produce arecovered data segment.

FIG. 43A is a schematic block diagram of an embodiment of a dispersedstorage network (DSN) that includes the distributed storage and task(DST) processing unit 16 of FIG. 1, the network 24 of FIG. 1, and aplurality of storage pools 1-2. The DST processing unit 16 includes theDST client module 34 of FIG. 1. Each storage pool includes a set of DSTexecution (EX) units, where a number of DST execution units of the setof DST execution units is in accordance with a storage approach. EachDST execution unit may be implemented utilizing the DST execution unit36 of FIG. 1. The DSN functions to access data (e.g., store the data,retrieve the data, delete the data) utilizing the plurality of pools inaccordance with the storage approach.

The storage approach includes establishing a set of dispersal parametersof a dispersed storage error coding function such that associated accessresults may be obtained. Such results includes a data retrievalreliability centric focus and a speed of access centric focus. Forexample, the storage pool 1 is associated with the data retrievalreliability centric focus when associated dispersal parameters includesan information dispersal algorithm (IDA) width of 28 and a decodethreshold level of 20. As another example, the storage pool 2 isassociated with the speed of access centric focus when associateddispersal parameters includes an IDA width of 3 and a decode thresholdlevel of 2.

In an example of operation of the accessing of the data, the DST clientmodule 34 receives data for storage. The receiving includes at least oneof receiving an access data request 460, where the access data request460 includes a store data request. The store data request includes oneor more of the data, a data identifier of the data, and a storageapproach preference.

Having received the data for storage, the DST client module 34determines the storage approach (e.g., selecting a storage pool) for thedata based on the data (e.g., and based on characteristics of thestorage pools). The determining includes at least one of indicating toutilize storage pool 2 when a data size is less than a small data sizemaximum threshold level, indicating to utilize storage pool 2 when thedata size is greater than a large data size minimum threshold level,selecting storage pool based on a requested storage pool approachpreference, indicating storage pool 1 when an expected frequency of dataaccess is greater than an access threshold level, interpreting systemregistry information to determine a storage pool selection, utilizing apredetermination which may include storing the data in both storagepools substantially simultaneously, establishing updated dispersalparameters in accordance with updated system registry information, anddetermining the dispersal parameters based on the data (e.g., for adesired level of retrieval reliability versus access performance).

Having determined the storage approach for the data, the DST clientmodule 34 facilitates storage of the data in one or more of the storagepools in accordance with the storage approach. For example, the DSTclient module 34 dispersed storage error encodes the data utilizingdispersal parameters associated with the storage approach to produce aplurality of sets of encoded data slices and sends, via the network 24,slice access requests 462 that includes the plurality of sets of encodeddata slices to DST execution units of the selected storage pool forstorage.

Having stored the data, the DST client module 34 determines to recoverthe data from at least one of the storage pools. The determiningincludes at least one of receiving another access request that includesa read data request, detecting loss of the data from at least one of thestorage pools, and interpreting a consistency check scheduled tofacilitate updating consistency between the data stored on two or moreof the storage pools.

Having determined to recover the data, the DST client module 34 selectsa storage pool of the plurality of storage pools for recovery of thedata. The selecting includes at least one of randomly selecting when thedata is greater than a large data size threshold level and recoveryperformance within a timeframe is not required, selecting the firststorage pool when a most up-to-date version is required, selecting thesecond storage pool when retrieval performance is to be maximized,selecting the first storage pool in a time frame since a last retrievalis greater than a retrieval time threshold level, and selecting anotherstorage pool when data losses are detected from a storage pool.

Having selected the storage pool, the DST client module 34 facilitatesrecovery of the data from the selected storage pool. For example, theDST client module 34 issues, via the network 24, slice access requests462 that includes read slice requests to the DST execution units of theselected storage pool, receives slice access responses 464 that includesread slice responses, and for each set of encoded data slices, dispersedstorage error decodes a decode threshold number of received encoded dataslices to reproduce the data, and issues an access data response 466that includes the reproduced data.

FIG. 43B is a flowchart illustrating an example of accessing datautilizing a plurality of storage pools. The method includes step 472where a processing module (e.g., of a distributed storage and task (DST)client module) receives data for storage in one or more sets of storageunits. The receiving includes receiving one or more of the data, a dataidentifier, a data size indicator, and a storage approach preference.The method continues at step 474 where the processing module determinesa storage approach for the data based on the data. The determiningincludes basing the determination on one or more of a predetermination,system registry information, the storage approach preference, and arequest (e.g., use a set of storage units and associated dispersalparameters associated with high-performance when the data is smallerthan a small size threshold, use another set of storage units anddispersal parameters when high retrieval reliability is required).

The method continues at step 476 where the processing module facilitatesstorage of the data in the one or more sets of storage units inaccordance with the storage approach. For example, the processing moduleencodes the data utilizing associated dispersal parameters of thestorage approach to produce a plurality of sets of encoded data slicesand sends the plurality of sets of encoded data slices to a set ofstorage units affiliated with the storage approach.

The method continues at step 478 where the processing module determinesto recover the data from at least one of the sets of storage units. Thedetermining includes at least one of receiving a data access request,detecting loss of data, and determining to facilitate a consistencycheck function.

The method continues at step 480 where the processing module selects aset of storage units for recovery of the data. For example, theprocessing module selects a set of storage units associated with highretrieval reliability when a most recent revision is required, the datasize is larger than a large data size threshold level, and a time framesince a last retrieval is greater than a retrieval time frame thresholdlevel. As another example, the processing module selects a set ofstorage units associated with high-performance when high-performance isrequired and a DSN system activity level indicator indicates that a highlevel of system activity exists.

The method continues at step 482 where the processing module facilitatesrecovery of the data from the selected set of storage units. Forexample, the processing module issues read slice requests to theselected set of storage units, receives read slice responses, and foreach set of encoded data slices, dispersed storage error decodes adecode threshold number of received encoded data slices to reproduce adata segment of the data.

FIGS. 44A and 44B are schematic block diagrams of another embodiment ofa dispersed storage network (DSN) that includes the distributed storageand task (DST) processing unit 16 of FIG. 1 and a storage pool 490. TheDST processing unit 16 includes the DST client module 34 of FIG. 1. Thestorage pool includes a set of DST execution (EX) units 1-6 whendispersal parameters of a dispersed storage error coding functionutilized to store data in the set of DST execution units includes aninformation dispersal algorithm (IDA) width n=6. Each DST execution unitmay be implemented utilizing the DST execution unit 36 of FIG. 1. TheDSN functions to access data in the storage pool. The accessing includesstoring the data in the storage pool and retrieving the data from thestorage pool. The data may include one or more data objects.

FIG. 44A illustrates steps of an example of storing the data in thestorage pool where the DST client module 34 generates a data segment toinclude a first data object A for storage and one or more future dataobjects, where a decode threshold number of data objects includes thefirst data object A and the one or more future data objects and wherethe dispersal parameters further includes the decode threshold number.The DST client module 34 generates each of the one or more future dataobjects to include all zeros. For example, the DST client module 34generates the data segment to include the first data object A and allzeros for a decode threshold number k minus one number of future dataobjects. For instance, the DST client module 34 generates two futuredata objects of all zeros when the decode threshold number is three.

Having generated the data segment, the DST client module 34 dispersedstorage error encodes the data segment utilizing the dispersed storageerror coding function and the dispersal parameters to produce a set ofencoded data slices 492, where the dispersed storage error codingfunction includes matrix multiplication of the data segment by anencoding matrix and where the encoding matrix includes a unity matrix ina most significant number of a decode threshold number of rows. Forinstance, the DST client module 34 generates a set of encoded dataslices to include a first encoded data slice that is essentially thesame as the first data object A, a second encoded data slice that is allzeros, a third encoded data slice that is all zeros, a fourth encodeddata slice that is an error coded slice 4A corresponding to a fourth rowof the encoding matrix, and a fifth encoded data slice that is an errorcoded slice 5A corresponding to a fifth row of the encoding matrix.

Having produced the set of encoded data slices 492, the DST clientmodule 34 facilitates storage of the set of encoded data slices 492 inthe set of DST execution units of the storage pool. For example, the DSTclient module 34 generates a set of write slice requests that includesthe set of encoded data slices 492 and sends the set of write slicerequests to the storage pool. The sending may include transmitting theset of write slice requests via the network 24 of FIG. 1 to the DSTexecution units of the storage pool.

FIG. 44B illustrates further steps of the example of storing the data inthe storage pool where the DST client module 34 facilitates storage of asecond data object B as a future data object. For example, the DSTclient module 34 sends slice update information 494 that includes thesecond data object B to the DST execution unit 2 for storage, where theDST execution unit 2 performs an exclusive OR (XOR) function on thesecond data object B with a retrieved stored future data object (e.g.,all zeros) and overwrites the stored future data object with the resultof the XOR function. Alternatively, the DST execution unit 2 stores thereceived second data object B by overwriting the stored future dataobject.

For each stored n-k error coded slice, the DST client module 34calculates a partial contribution of the second data object B inaccordance with a partial encoding approach. For example, the DST clientmodule 34 matrix multiplies a row of the encoding matrix thatcorresponds to the error coded slice by the second data object B toproduce the partial contribution. For instance, the DST client module 34matrix multiplies a fourth row of the encoding matrix by the second dataobject B to produce the partial contribution for the error coded slice4A that is stored in the DST execution unit 5.

For each stored n-k error coded slice, the DST client module 34facilitates updating of the slice utilizing the corresponding calculatedpartial contribution. The facilitating includes the DST client module 34sending further slice update information 494 that includes the partialcontribution to a corresponding DST execution unit, where the DSTexecution unit performs the XOR function on a corresponding stored errorcoded slice with the partial contribution to produce an updated errorcoded slice for overwriting of the error coded slice. For example, theDST client module 34 sends the partial contribution for the error codedslice 4A to the DST execution unit 5, where the DST execution unit 5performs the XOR function on the error coded slice 4A with the receivedpartial contribution for the error coded slice 4A to produce an updatederror coded slice 4AB for overwriting of the error coded slice 4A.

Alternatively, or in addition to, recovery of a data object includesaccessing a corresponding DST execution unit to recover the data object(e.g., issuing a read slice request to DST execution unit 2 to recoverthe second data object B) or, when the data object is unavailabledirectly (e.g., a storage error is associated with storage of the seconddata object B), recovering a decode threshold number of encoded dataslices of the set of encoded data slices (e.g., encoded data slices 1,3, and 5), and dispersed storage error decoding the decode thresholdnumber of encoded data slices to reproduce the desired data object.

FIG. 44C is a flowchart illustrating an example of accessing data. Themethod includes step 506 where a processing module (e.g., of adistributed storage and task (DST) client module) generates a datasegment to include a first data object for storage in one or more futurenull data objects. For example, the processing module establishes thedata segment such that a first encoded data slice is substantially thesame as the first data object and further future null data objects areall zeros.

The method continues at step 508 where the processing module dispersedstorage error encodes the data object to produce a set of encoded dataslices, where a first encoded data slice is substantially the same asthe first data object and where the set of encoded data slices includesn-k error coded slices. For example, the processing module matrixmultiplies the data segment by an encoding matrix, where the encodingmatrix includes a unity matrix, with a decode threshold numberdimension, in a most significant number of rows, to produce the set ofencoded data slices.

The method continues at step 510 where the processing module facilitatesstorage of the set of encoded data slices in a set of storage units. Forexample, the processing module sends the set of encoded data slices tothe set of storage units for storage. The method continues at step 512where the processing module facilitates storage of a second data object,where a null data object is overwritten with the second data object. Forexample, the processing module generates an update slice request toinclude the second data object, where a second encoded data slice is tobe substantially the same as the second data object, and sends theupdate slice request to a corresponding storage unit where the storageunit performs an exclusive OR function on the corresponding null dataobject with the second data object to produce a second encoded dataslice for storage.

For each of the error coded slices, the method continues at step 514where the processing module calculates a partial contribution of thesecond data object in accordance with a partial encoding approach. Forexample, the processing module matrix multiplies a corresponding row ofthe encoding matrix by the second data object to produce the partialcontribution. For each of the error coded slices, the method continuesat step 516 where the processing module facilitates updating the errorcoded slice utilizing a corresponding partial contribution. For example,the processing module issues an update slice request to a correspondingstorage unit where the storage unit performs the exclusive OR functionon the partial contribution with the stored error coded slice to producean updated error coded slice for storage in the storage unit.

Alternatively, or in addition to, recovery of a data object includesaccessing a corresponding storage unit to recover the data object or,when the data object is unavailable directly, recovering a decodethreshold number of encoded data slices of the set of encoded dataslices and dispersed storage error decoding the decode threshold numberof encoded data slices to reproduce the desired data object.

FIG. 45A is a schematic block diagram of another embodiment of adispersed storage network (DSN) that includes the distributed storageand task (DST) processing unit 16 of FIG. 1, the network 24 of FIG. 1,and a storage pool 524. The DST processing unit 16 includes the DSTclient module 34 of FIG. 1. The storage pool 524 includes a set of DSTexecution (EX) units 1-5. Alternatively, the set of DST execution unitsmay include any number of DST execution units in accordance with adispersed storage error coding function that includes dispersalparameters. Each DST execution unit may be implemented utilizing the DSTexecution unit 36 of FIG. 1. The dispersal parameters includes aninformation dispersal algorithm (IDA) width n, and an IDA decodethreshold. For instance, the storage pool may include 28 DST executionunits when the IDA width is 28.

The DSN functions to access serially stored data. The accessing includesstoring data 526 and retrieving at least a portion of the data toproduce a recovered data portion 534. The data 526 includes a pluralityof data blocks. Each data block includes one or more data bytes. Forexample, the DST processing unit 16 receives the data 526 that includesdata blocks D1, D2, D3, etc.

In an example of operation of the storing of the serially stored data,the DST client module 34 determines to serially store received data 526in the storage pool 524. The determining includes at least one ofindicating to serially store when a data size of the received data 526is greater than a serial storage threshold level, receiving a request,and interpreting a data retrieval requirement (e.g., required to recovera small portion of the data, i.e., an IDA decode threshold number ofdata blocks rather than a larger data segment that includes a pluralityof the IDA decode threshold numbers of data blocks).

For every IDA decode threshold number of received data blocks of thedata, the DST client module 34 dispersed storage error encodes the IDAdecode threshold number of received data blocks to produce correspondingerror coded slices 528 in accordance with a dispersed storage errorcoding function. For example, the DST client module 34 matrix multipliesthe IDA decode threshold number of received data blocks by acorresponding row of an encoding matrix to produce a corresponding errorcoded slice. For instance, the DST client module 34 matrix multipliesdata blocks D1, D2, and D3 by a fourth row of the encoding matrix toproduce an error coded slice 4-1 and matrix multiplies the data blocksD1, D2, and D3, by a fifth row of the encoding matrix to produce anerror coded slice 5-1 when the IDA decode threshold number is 3 and theIDA width is 5. The encoding of the IDA decode threshold number ofreceived data blocks is discussed in greater detail with reference toFIG. 45B.

For each IDA decode threshold number of received data blocks, the DSTclient module 34 facilitates storage of the IDA threshold number ofreceived data blocks and the corresponding n-k error coded slices in thestorage pool. For example, the DST client module 34 sends, via thenetwork 24, an initial set of encoded data slices 528 to the set of DSTexecution units for storage, sends subsequent sets of slices as sliceappends 530, where each DST execution unit appends a correspondingsubsequently received slice to a previously received slice to create anupdated appended encoded data slice that is stored in the DST executionunit. For instance, the DST client module 34 sends, via the network 24,an encoded data slice of data block D1 to the DST execution unit 1 forstorage, followed by an encoded data slice of data block D4 to the DSTexecution unit 1 for appending to the data block D1, followed by anencoded data slice of data block D7 to the DST execution unit 1 forappending to the data blocks D1 and D7 etc., as the serial data blocksare received and encoded.

In an example of operation of the retrieving of the serially storeddata, the DST client module 34 identifies a portion of the data forrecovery. For example, the DST client module 34 receives a retrievalrequest and identifies one or more of the IDA decode threshold number ofdata blocks for recovery that are associated with the portion of thedata for recovery. The identifying includes at least one of accessing adirectory, accessing metadata to identify a data size indicator,accessing the data to search for the portion based on one or more indexkeys, and receiving a read offset preference. For example, the DSTclient module 34 identifies the second decode threshold number of datablocks (e.g., data blocks D4, D5, and D6) associated with the portion ofthe data for recovery.

Having identified the portion of the data for recovery, the DST clientmodule 34 generates a read offset for each of the set of appendedencoded data slices based on the identified portion of the data forrecovery. The generating includes identifying an offset to a desireddata block of an appended encoded data slice based on the identified oneor more IDA decode threshold number of data blocks. The generating mayfurther include utilizing the read offset preference to produce the readoffset.

Having generated the read offset, the DST client module 34 retrieves adecode threshold number of data blocks of a set of data blockscorresponding to the read offset. For example, the DST client module 34generates a decode threshold number of read slice requests that includesthe read offsets, sends, via the network 24, the read slice requests tothe storage pool, and receives, via the network 24, at least a decodethreshold number of data blocks 532 (e.g., may include multiple sets ofdata blocks as the DST execution unit receives a data block sequentiallystored in a memory of the DST execution unit).

Having received the decode threshold number of data blocks 532, the DSTclient module 34, for each set of data blocks, dispersed storage errordecodes the received decode threshold number of data blocks to reproducereceived data blocks of the identified portion of the data for recoveryto produce the recovered data portion 534. For example, the DST clientmodule 34 dispersed storage error decodes slice D4, slice D6, and errorcoded slice 4-2 to reproduce data blocks D4, D5, and D6.

FIG. 45B is a diagram illustrating an example of matrix multiplicationof an encoding matrix (E) and a data matrix (D) to produce a codedmatrix (C) of encoded data blocks. A data segment that includes aserially received IDA decode threshold number of data blocks D1 throughDk is arranged in success of columns of the data matrix (D). Forexample, the data blocks D1-D3 are included in a first column, the datablocks D4-D6 are included in a second column, the data blocks D7-D9 areincluded in a third column, and data blocks D10-D12 are included in afourth column of the data matrix. The encoding matrix includes a unitymatrix in a most significant IDA decode threshold number of rows andcolumns.

The encoding function may utilize a variety of encoding approaches tofacilitate dispersed storage error encoding of data. The encodingfunction includes, but is not limited to, at least one of Reed Solomonencoding, an information dispersal algorithm, on-line codes, forwarderror correction, erasure codes, convolution encoding, Trellis encoding,Golay, Multidimensional parity, Hamming, Bose Ray Chauduri Hocquenghem(BCH), and/or Cauchy-Reed-Solomon.

In an example of a Reed Solomon encoding function, the matrixmultiplication is utilized to encode a data segment to produce a set ofencoded data blocks as a representation of the data segment. The ReedSolomon encoding function is associated with an error coding number(e.g., IDA width, number of slices per set) and an IDA decode thresholdnumber. As a specific example, the encoding matrix includes the errorcoding number of Y rows and the decode threshold number of X columns.Accordingly, the encoding matrix includes Y rows of X coefficients. Theset of data blocks of the data segment is arranged into the data matrixhaving X rows of Z number of data words (e.g., X*Z=number of datablocks). The data matrix is matrix multiplied by the encoding matrix toproduce the coded matrix, which includes Y rows of Z number of encodedvalues (e.g., encoded data blocks 540).

When utilizing the unity matrix within the encoding matrix, the codedmatrix includes an IDA decode threshold number of rows that issubstantially the same as the data matrix and n-k rows of error codedslices. The coded matrix may be generated in a sequential fashion asdata is received for the encoding. For example, the first column of thecoded matrix may generate five encoded data slices 1-5, where theencoded data slices includes D1, D2, D3, X41, and X51 when thecorresponding received data includes data blocks D1, D2, and D3. Theencoded data slices 1-5 may be updated by appending further encoded datablocks 542 as more data is received. For example, a second column of thecoded matrix that includes D4, D5, D6, X42, and X52 may be generatedwhen receiving corresponding data blocks D4, D5, and D6. The appendingresults in an updated encoded data slice 1 that includes D1 and D4, anupdated encoded data slice 2 that includes D2 and D5, etc.

FIG. 45C is a flowchart illustrating an example of accessing seriallystored data. The method includes step 550 where a processing module(e.g., of a distributed storage and task (DST) client module) determinesto serially store received data in a set of storage units utilizing adispersed storage error coding function. For example, the processingmodule receives a store data request and indicates to serially storewhen a data size of the received data is greater than a serial storagethreshold level.

For every information dispersal algorithm (IDA) threshold number (e.g.,a decode threshold number) of received data blocks, the method continuesat step 552 where the processing module encodes the IDA threshold numberof received data blocks using the dispersed storage error codingfunction to produce a set of encoded data slices. For example, theprocessing module receives a next IDA threshold number of data blocksand encodes the IDA threshold number of data blocks using the dispersedstorage error coding function to produce the set of encoded data slices.

For every IDA decode threshold number of received data blocks, themethod continues at step 554 where the processing module facilitatesstorage of the corresponding sets of encoded data slices in the set ofstorage units, where each storage unit appends a plurality of receivedencoded data slices to an appended encoded data slice for storage. Forexample, the processing module issues write slice requests to the set ofstorage units for a first set of encoded data slices and issues sliceappend requests for the subsequent sets of encoded data slices.

The method continues at step 556 where the processing module identifiesa portion of the data for recovery from the set of storage units. Forexample, the processing module interprets a retrieval request toidentify at least one IDA threshold number of received data blocks forrecovery. The method continues at step 558 where the processing modulegenerates a read offset for the set of appended encoded data slicesbased on the identified portion of the data for recovery. For example,the processing module generates the read offset based on a number of IDAthreshold number of received data blocks from a first IDA thresholdnumber of appended encoded data slices to the identified at least oneIDA threshold number of received data blocks for recovery.

The method continues at step 560 where the processing module retrieves adecode threshold number of data blocks of a set of data blockscorresponding to the read offset within the set of appended encoded dataslices. For example, the processing module issues read slice requeststhat includes the read offset to the set of storage units and receivesat least one of the decode threshold number of data blocks.

The method continues at step 562 where the processing module dispersedstorage error decodes the retrieved decode threshold number of datablocks to produce the portion of the data for recovery. For example, foreach set of data blocks, the processing module dispersed storage errordecodes the received decode threshold number of data blocks to reproducereceived data blocks of the portion of the data for recovery.

FIGS. 46A and 46B are schematic block diagrams of another embodiment ofa dispersed storage network (DSN) that includes the distributed storageand task (DST) processing unit 16 of FIG. 1 and a storage pool 570. TheDST processing unit 16 includes the DST client module 34 of FIG. 1. Thestorage pool includes a set of DST execution (EX) units. Each DSTexecution unit may be implemented utilizing the DST execution unit 36 ofFIG. 1. The storage pool may include a number of DST EX units inaccordance with an information dispersal algorithm (IDA) width numberwhen data is encoded using a dispersed storage error coding function toproduce one or more sets of encoded data slices for storage in the setof DST EX units, where a decode threshold number of encoded data slicesof each set of encoded data slices is required to recover the data. Forexample, the set of DST EX units includes DST EX units 1-16 and 10encoded data slices are required from each set for data recovery whenthe IDA width is 16 and the decode threshold is 10. Each DST EX unit maybe implemented using the DST EX unit 36 of FIG. 1. The DSN functions tooptimize the data storage.

FIG. 46A illustrates steps of an example of operation of the optimizingof the data storage where the DST client module 34 dispersed storageerror encodes a data segment of the data to produce a set of encodeddata slices (e.g., encoded data slices 1-16). Having produced the set ofencoded data slices, the DST client module 34 facilitates storage ofgreater than a write threshold number of encoded data slices of the setof encoded data slices, where the write threshold number is greater thanthe decode threshold number and less than the IDA width number. Forexample, the write threshold may be 13 when the IDA width is 16 and thedecode threshold is 10. The data retrieval reliability is a function ofa difference between the write threshold and the decode threshold andwrite availability is a function of a difference between the IDA widthand the write threshold.

The facilitating of the storage of the greater than a write thresholdnumber of encoded data slices includes generating a set of write slicerequests 572 that includes the set of encoded data slices, sending(e.g., via the network 24 of FIG. 1) the set of write slice requests 572to the set of DST execution units 1-16, receiving write slice responses574 from at least some of the DST execution units, identifying a numberof favorably stored encoded data slices of the set of encoded dataslices based on the received write slice responses 574, and sendinganother write slice request 572 when the number of favorably storedencoded data slices is less than or equal to the write threshold. Forexample, the DST client module 34 facilitates storage of all 16 encodeddata slices except for encoded data slice 15 when a storage error occursfor encoded data slice 15 and the write threshold is 13.

FIG. 46B illustrates further steps of the example of operation of theoptimizing of the data storage where the DST client module 34 determinesa number of stored encoded data slices for deletion. The determiningincludes calculating a difference between the number of favorably storedencoded data slices and a write threshold number. For example, the DSTclient module 34 determines to delete to encoded data slices when thenumber of favorable encoded data slices is 15 and the write threshold is13.

Having determined the number of stored encoded data slices for deletion,the DST client module 34 selects the number of stored encoded dataslices for deletion from the favorably stored encoded data slices. Theselecting may be based on one or more of performing a random selection,a predetermination, and selecting error coded slices when the unitymatrix is utilized in an encoding matrix of the dispersed storage errorcoding function (e.g., encoded data slices 11-16 as candidates of errorcoded slices). For example, the DST client module 34 selects encodeddata slices 14 and 16 for deletion when encoded data slices 11-16 areerror coded slices.

Having selected the stored encoded data slices for deletion, the DSTclient module 34 facilitates deletion of the selected number of storedencoded data slices for deletion. The facilitating of the deletionincludes the DST client module 34 issuing delete slice requests 576(e.g., delete slice, a rollback request, an undo request) to DSTexecution units associated with the selected number of stored encodeddata slices for deletion, receiving delete slice responses 578, andretrying sending a particular delete slice request 576 when notreceiving an indication of favorable deletion of an encoded data slicewithin a deletion time frame.

Having successfully deleted the selected encoded data slices fordeletion, the DST client module 34 has completed a trimming process tomaintain the number of stored encoded data slices of the set of encodeddata slices at the write threshold number. The DST client module 34 mayfurther indicate that the rebuilding of encoded data slices of the setof encoded data slices beyond the successfully stored and maintainedwrite threshold number is unnecessary. For example, a rebuilding processrebuilds encoded data slice 9 when encoded data slice 9 is associatedwith a storage error, but does not rebuild encoded data slices 14, 15,and 16 when the DST client module 34 has indicated that the rebuildingof encoded data slices of the set of encoded data slices beyond thesuccessfully stored and maintained write threshold number isunnecessary.

FIG. 46C is a flowchart illustrating an example of optimizing datastorage. The method includes step 584 where a processing module (e.g.,of a distributed storage and task (DST) client module) dispersed storageerror encodes a data segment to produce a set of encoded data slices inaccordance with an IDA width and a decode threshold number. The methodcontinues at step 586 where the processing module facilitates storage ofmore than a write threshold number of encoded data slices of the set ofencoded data slices in a set of storage units. For example, theprocessing module generates more than a write threshold number of writeslice requests that includes encoded data slices of the set of encodeddata slices and sends the write slice requests to the set of storageunits to facilitate at least a storage of greater than a write thresholdnumber of encoded data slices.

The method continues at step 588 where the processing module identifiesa number of favorably stored encoded data slices. For example, theprocessing module receives write slice responses and interprets thewrite slice responses to produce the number of favorably stored encodeddata slices. The method continues at step 590 where the processingmodule determines a number of stored encoded data slices for deletion.For example, the processing module calculates a difference between thenumber of favorably stored encoded data slices and the write thresholdnumber as the number for deletion.

The method continues at step 592 where the processing module selects thenumber of stored encoded data slices for deletion from the favorablystored encoded data slices. The processing module may perform theselection based on a selection scheme. The processing module may choosethe selection scheme based on at least one of a predetermined pattern,interpreting system registry information, and receiving instructions.The selection schemes include choosing error coded slices that are notstrictly data, performing a random selection, and selecting slicesassociated with porous performing storage units.

The method continues at step 594 where the processing module facilitatesdeletion of the selected number of stored encoded data slices fordeletion. For example, the processing module identifies storage unitsassociated with the selected number of stored encoded data slices fordeletion (e.g., performs a lookup in a slice location table), issuesdelete requests to the identified storage units to delete the selectednumber of stored encoded data slices for deletion (e.g., where thedelete request includes one or more of slice names and revision levels),receives delete responses, and performs a retry deletion sequence whennot receiving a favorable number of delete responses within a responsetimeframe.

FIGS. 47A, 47B, and 47C are schematic block diagrams of anotherembodiment of a dispersed storage network (DSN) that includes thedistributed storage and task (DST) processing unit 16 of FIG. 1, thenetwork 24 of FIG. 1, and a plurality of sites 1-3. The DST processingunit 16 includes the DST client module 34 of FIG. 1. Each of theplurality of sites includes a set of DST execution (EX) units, where atotal number of DST execution units is in accordance with an informationdispersal algorithm (IDA) width of a dispersed storage error codingfunction. For example, the plurality of sites includes DST executionunits 1-24 when the IDA width is 24. Each DST execution unit may beimplemented utilizing the DST execution unit 36 of FIG. 1. The DSNfunctions to optimize data storage in the set of DST execution units.

FIG. 47A illustrates steps of an example operation of the optimizing ofthe data storage where the DST client module 34 dispersed storage errorencodes a data segment of the data to produce a set of encoded dataslices 604, where at least a decode threshold number of encoded dataslices are required for recovery of the data segment and where the setof encoded data slices 604 includes an IDA width number of encoded dataslices. For example, the DST client module 34 dispersed storage errorencodes the data segment to produce encoded data slices 1-24.

Having produced the set of encoded data slices 604, the DST clientmodule 34 obtains DSN configuration information. The obtaining includesat least one of receiving system registry information, receiving a queryresponse, and performing a lookup. The DSN configuration informationincludes one or more of a number of sites of the plurality of sites anda number of DST execution units per site.

Having obtained the DSN configuration information, the DST client module34 maps a write threshold number of encoded data slices of the set ofencoded data slices to a write threshold number of DST execution unitsof the set of DST execution units in accordance with an affinityfunction to produce a slice storage mapping. For example, the DST clientmodule 34 performs a deterministic function on one or more of anidentifier associated with the data segment, the IDA width, and thenumber of sites to produce the slice storage mapping, where encoded dataslices are to be stored in an equi-distributed set of DST executionunits with a uniform probability distance function across all possibledata segment identifiers. For instance, the DST client module 34produces the slice storage mapping to include mapping encoded dataslices 1-5 to DST execution units 1-5 of site 1, encoded data slices9-13 to DST execution units 9-13 of site 2, and encoded data slices17-21 to DST execution unit 17-21 of site 3 when the write threshold is15 and a number of sites is 3 (e.g., evenly distributed).

Having produced the slice storage mapping, the DST client module 34facilitates storage of the write threshold number of encoded data slicesto the write threshold number of DST execution units in accordance withthe slice storage mapping. For example, DST client module 34 generates awrite threshold number of write slice requests that includes theassociated write threshold number of encoded data slices, sends, via thenetwork 24, the write threshold number of write slice requests tocorresponding DST execution units for storage (e.g., encoded data slices1-5 to DST execution units 1-5, encoded data slices 9-13 to DSTexecution units 9-13, and encoded data slices 17-21 to DST executionunits 17-21), identifies favorably stored encoded data slices (e.g., byinterpreting write slice responses), issues one or more additional writeslice requests when a number of favorably stored encoded data slices isless than the write threshold number, or deletes favorably storedencoded data slices when more than the write threshold number of encodeddata slices have been stored (e.g., when issuing more than the writethreshold number of write slice requests). Having successfullyfacilitated storage of the read threshold number of encoded data slices,the DST client module 34 may indicate that the write threshold number ofencoded data slices have been successfully stored.

FIG. 47B illustrates further steps of the example of operation of theoptimizing of the data storage where the DST client module 34 detectsunavailability of one or more encoded data slices of the write thresholdnumber of successfully stored encoded data slices. The detectingincludes at least one of detecting a site failure, interpreting an errormessage, detecting a failure of one or more DST execution units, anddetecting a storage error (e.g., a missing encoded data slice, acorrupted encoded data slice). For example, the DST client module 34detects unavailability of encoded data slices 9-13 when interpreting anerror message indicating that site 2 is temporarily unavailable.

Having detected the unavailability of the one or more encoded dataslice, the DST client module 34 obtains updated DSN configurationinformation. The obtaining of the updated DSN configuration informationincludes at least one of interpreting an updated test result,interpreting updated site availability information, interpreting updatedDST execution unit availability information, interpreting updated systemregistry information, interpreting a further received query response,and performing a local lookup.

Having obtained the updated DSN configuration information, the DSTclient module 34 maps an updated write threshold number of encoded dataslices of the set of encoded data slices to an updated write thresholdnumber of available DST execution units in accordance with the affinityfunction and based on the updated DSN configuration information toproduce an updated slice storage mapping. For example, the DST clientmodule 34 produces the updated slice storage mapping to indicate storageof 8 encoded data slices at site 1 (e.g., encoded data slices 1-8), zeroencoded data slices at site 2, and 7 encoded data slices at site 3(e.g., encoded data slices 17-23; totaling 15 encoded data slices of thewrite threshold number distributed in a substantially equal fashionamongst available sites).

Having produced the updated slice storage mapping, the DST client module34 facilitates storage of the updated write threshold number of encodeddata slices 604 to the write threshold number of available DST executionunits in accordance with one or more of the updated slice storagemapping and the slice storage mapping (e.g., where already storedencoded data slices are located). The facilitating includes generatingone or more additional encoded data slices for encoded data slices 604of the updated slice storage mapping that are not included in the slicestorage mapping and storing the additional encoded data slices incorresponding DST execution units of available sites. For example, theDST client module 34 retrieves a decode threshold number of encoded dataslices of the set of encoded data slices (e.g., encoded data slices 1-5from site 1, encoded data slices 17-21 from site 3) when the decodethreshold is 10, dispersed storage error decodes the retrieved decodethreshold number of encoded data slices to reproduce the data segment,dispersed storage error encodes the reproduced data segment to reproducethe set of encoded data slices, sends, via the network 24, the one ormore additional encoded data slices 604 to the corresponding DSTexecution units (e.g., encoded data slices 6-8 to DST execution units6-8, encoded data slices 22-23 to DST execution units 22-23).

Alternatively, or in addition to, further received data for storage whenreceived while the site 2 is temporarily unavailable, may be stored in asimilar fashion as described above. For example, the DST client module34 encodes the further received data for storage to produce further setsof encoded data slices, selects a write threshold number of the furthersets of encoded data slices, and facilitates storage of the selectedread threshold number of further sets of encoded data slices 604 in theavailable sites 1 and 3.

FIG. 47C illustrates further steps of the example of operation of theoptimizing of the data storage where the DST client module 34 detectsavailability of greater than the write threshold number of encoded dataslices of the set of encoded data slices. The detecting includes one ormore of detecting availability of a previously failed site, detectingavailability of a previously temporarily unavailable site, interpretingan error message, detecting availability of one or more previouslyunavailable DST execution units, interpreting list slice responses,interpreting read slice responses, and detecting abatement of a storageerror. For example, the DST client module 34 detects availability ofencoded data slices 9-13 at site 2 when detecting that the previouslytemporarily unavailable site 2 is now available. As such, the DST clientmodule 34 detects that greater than the write threshold number ofencoded data slices are available when detecting that encoded dataslices 1-13 and 17-23 are available (e.g., 20 available encoded dataslices is greater than a write threshold of 15).

Having detected the availability of the greater than the write thresholdnumber of encoded data slices, the DST client module 34 obtains furtherupdated DSN configuration information. Having obtained the furtherupdated DSN configuration information, the DST client module 34 maps afurther updated write threshold number of encoded data slices of the setof encoded data slices to a further updated write threshold number ofavailable DST execution units in accordance with the affinity functionand based on the further updated DSN configuration information toproduce a further updated slice storage mapping. For example, theprocessing module maps encoded data slices 1-5 to site 1, encoded dataslices 9-13 to site 2, and encoded data slices 17-21 to site 3 tosubstantially even distribute the write threshold number of encoded dataslices amongst the three sites.

Having produced the further updated slice storage mapping, the DSTclient module 34 facilitates storage of the further updated writethreshold number of encoded data slices to the further updated writethreshold number of available DST execution units in accordance with thefurther updated slice storage mapping. The facilitating includes one ormore of deleting one or more encoded data slices when the number ofavailable encoded data slices is greater than the read threshold numberand/or rebuilding one or more encoded data slices for storage in the oneor more DST execution units of the set of DST execution units inaccordance with the further updated slice mapping. For example, the DSTclient module 34 issues delete slice requests 606 for encoded dataslices 6-8 to DST execution units 6-8 and for encoded data slices 22-23to DST execution units 22-23. As a result, storage of the writethreshold number of encoded data slices is maintained in a uniformlydistributed fashion. Alternatively, the DST client module 34 may rebuildencoded data slices 9-13 when the encoded data slices 9-13 areunavailable as the previously temporarily unavailable site 2 becomesavailable.

FIG. 47D is a flowchart illustrating another example of optimizing datastorage. The method includes step 610 where a processing module (e.g.,of a distributed storage and task (DST) client module) dispersed storageerror encodes a data segment to produce a set of encoded data slices.The method continues at step 612 where the processing module obtains DSNconfiguration information. For example, the processing module interpretsa query response. The method continues at step 614 where the processingmodule maps a write threshold number of encoded data slices to a writethreshold number of storage units to produce a slice storage mapping.For example, the processing module performs a deterministic function onan identifier associated with the data segment, an IDA width number, anda number of available sites to produce the slice storage mapping.

The method continues at step 616 where the processing module facilitatesstorage of the write threshold number of encoded data slices to thewrite threshold number of storage units in accordance with the slicestorage mapping. For example, the processing module issues write slicerequests to storage units of a set of storage units that includes thewrite threshold number of storage units, trims (e.g., deletes) encodeddata slices beyond the write threshold number, and retries storage ofencoded data slices when determining that the write threshold number hasnot yet been successfully stored.

The method continues at step 618 where the processing module detectsunavailability of one or more of the write threshold number of encodeddata slices. The detecting includes at least one of detecting a sitefailure, detecting a storage unit failure, and interpreting a slicestorage error. The method continues at step 620 where the processingmodule obtains updated DSN configuration information. For example, theprocessing module interprets a subsequent query response.

The method continues at step 622 where the processing module maps anupdated write threshold number of encoded data slices to an updatedwrite threshold number of storage units to produce an updated slicestorage mapping. For example, the processing module performs thedeterministic function based on currently available encoded data slicesof the write threshold number of encoded data slices to produce theupdated slice storage mapping. Alternatively, the processing moduleproduces a new mapping without regards to previously stored encoded dataslices.

The method continues at step 624 where the processing module facilitatesstorage of the updated write threshold number of encoded data slices tothe updated write threshold number of storage units in accordance withthe updated slice storage mapping. For example, the processing moduleidentifies encoded data slices of the updated write threshold number ofencoded data slices that have not been stored, rebuilds the identifiedencoded data slices, and sends the rebuilt encoded data slices tocorresponding storage units for storage.

The method continues at step 626 where the processing module detectsavailability of greater than the write threshold number of encoded dataslices. The detecting includes at least one of detecting availability ofa previously filled site, detecting availability of a previously filledstorage unit, detecting availability of a previously unavailable site,detecting availability of a previously unavailable storage unit, anddetecting availability of a previously unavailable encoded data slice.

The method continues at step 628 where the processing module obtainsupdated DSN configuration information. For example, the processingmodule interprets a still further query response. The method continuesat step 630 where the processing module maps a further updated writethreshold number of encoded data slices to a further updated writethreshold number of storage units to produce a further updated slicestorage mapping. For example, the processing module performs thedeterministic function further based on currently available encoded dataslices of the write threshold number of encoded data slices to producethe further updated slice storage mapping. Alternatively, the processingmodule produces a new slice storage mapping without regards topreviously stored and available encoded data slices.

The method continues at step 632 where the processing module facilitatesstorage of the further updated write threshold number of encoded dataslices to the further updated write threshold number of storage units inaccordance with the further updated slice storage mapping. Thefacilitating includes one or more of deleting previously stored encodeddata slices that are not included in the further updated slice storagemapping and rebuilding encoded data slices for encoded data slices thatare included in the further updated slice storage mapping but are notavailable.

FIGS. 48A, 48B, and 48C are schematic block diagrams of an embodiment ofa dispersed storage network (DSN) that includes the distribute storageand task (DST) processing unit 16 of FIG. 1 and a storage pool 640. TheDST processing unit 16 includes the DST client module 34 of FIG. 1. Thestorage pool 640 includes a set of DST execution (EX) units, where theset of DST execution units includes an information dispersal algorithm(IDA) width number of DST execution units. For example, the storage poolincludes DST execution units 1-36 when the IDA width is 36. The DSNfunctions to transition to an optimized data storage approach.

FIG. 48A illustrates steps of an example of operation of thetransitioning to the optimized data storage approach where the DSTclient module 34 dispersed storage error encodes a data segment of thedata to produce a set of encoded data slices, where at least a decodethreshold number of encoded data slices of the set of encoded dataslices are required for recovery of the data segment and where the setof encoded data slices includes an IDA width number of encoded dataslices. For example, the DST client module 34 encodes the data segmentto produce encoded data slices 1-36.

Having produced the set of encoded data slices, the DST client module 34maintains storage of the set of encoded data slices in the IDA widthnumber of DST execution units. For example, the DST client module 34issues a set of write slice requests to the set of DST execution units1-36, where the set of write slice requests includes the set of encodeddata slices. For instance, the DST client module 34 sends (e.g., via thenetwork 24 of FIG. 1) the encoded data slices 1-36 to the DST executionunits 1-36 for storage. Alternatively, the set of write slice requestsincludes a write threshold number of encoded data slices where the writethreshold number is less than the IDA width and greater than the decodethreshold (e.g., write threshold of 23 when the IDA width is 36 and thedecode threshold is 20). The maintaining further includes facilitatingstorage of a rebuilt encoded data slice when detecting a storage errorassociated with an encoded data slice of the set of encoded data slices.For example, the DST client module 34 rebuilds encoded data slice 2 whendetecting a storage error associated with encoded data slice 2 and sendsthe rebuilt encoded data slice 2 to the DST execution unit 2 forstorage. The maintaining further includes indicating that a rebuildinglevel of encoded data slices of the set of encoded data slices is to bemaintained at the IDA width number. For example, the DST client module34, or a rebuilding module, rebuilds any of the encoded data slices suchthat there are always 36 stored encoded data slices.

FIG. 48B illustrates further steps of the example of operation of thetransitioning to the optimized data storage approach where the DSTclient module 34 detects activation of an incremental subset of DSTexecution units for utilization in a trimmed write configuration of thestorage pool. The detecting includes at least one of interpreting anerror message, receiving an activation message, and interpreting systemregistry information (e.g., an updated IDA width of 45 is detected). Forexample, the DST client module 34 detects activation of DST executionunits 37-45 when interpreting a received activation message.

Having detected the activation of the incremental subset of DSTexecution units, the DST client module 34 generates a subset of rebuiltencoded data slices corresponding to the incremental subset of DSTexecution units. For example, the DST client module 34 obtains a decodethreshold number of encoded data slices, dispersed storage error decodesthe decode threshold number of encoded data slices to produce arecovered data segment, and dispersed storage error encodes therecovered data segment utilizing expanded rows of an encoding matrix toproduce the subset of rebuilt encoded data slices. For instance, the DSTclient module 34 recovers 20 encoded data slices, decodes the 20 encodeddata slices to produce the recovered data segment, dispersed storageerror encodes the recovered data segment to produce encoded data slices37-45.

Having generated the subset of rebuilt encoded data slices, the DSTclient module 34 maintains storage of an updated set of encoded dataslices that includes the set of encoded data slices and the subset ofrebuilt encoded data slices of the updated IDA width number of encodeddata slices. For example, the DST client module 34 sends the subset ofrebuilt encoded data slices (e.g., encoded data slices 37-45) tocorresponding DST execution units of the incremental subset of DSTexecution units (e.g., DST execution units 37-45) for storage andtemporarily updates the rebuilding level of encoded data slices as theupdated IDA width number (e.g., 45).

FIG. 48C illustrates further steps of the example of operation of thetransitioning to the optimized data storage approach where the DSTclient module 34 updates the write threshold number to produce anupdated write threshold number in accordance with a trimmed writeconfiguration of the storage pool 640. The updating includes at leastone of receiving an input, interpreting updated system registryinformation, and determining to activate the trimmed write configurationbased on a system performance level. For example, the DST client module34 updates the write threshold number from 23 to 30 when interpretingthe system registry information to activate the trimmed writeconfiguration of the storage pool.

Having updated the write threshold number, the DST client module 34maintains storage of the updated write threshold number of encoded dataslices. The maintaining includes updating the rebuilding level ofencoded data slices to the write threshold number (e.g., rather than theIDA width number) and trimming extra encoded data slices to facilitatethe maintaining of the number of encoded data slices at the writethreshold number. For example, the DST client module 34 issues deleteslice requests 31-45 to the DST execution units 31-45 to facilitatedeletion of encoded data slices 31-45 from the storage pool when thewrite threshold number of encoded data slices 1-30 are available.

FIG. 48D is a flowchart illustrating an example of transitioning to anoptimized data storage approach. The method includes step 646 where aprocessing module (e.g., of a distributed storage and task (DST) clientmodule) dispersed storage error encodes a data segment to produce a setof an IDA width number of encoded data slices. For example, theprocessing module encodes the data segment in accordance with dispersalparameters including the IDA width number, a write threshold number, anda decode threshold number.

The method continues at step 648 where the processing module maintainsstorage of the set of encoded data slices in a set of storage units. Forexample, the processing module stores the set of encoded data slices inthe set of storage units and maintains a rebuilding level of encodeddata slices at the IDA width level (e.g., enabling a rebuilding processto maintain storage of the full IDA width number of encoded dataslices).

The method continues at step 650 where the processing module detectsactivation of an incremental subset of storage units to produce anexpanded set of storage units in accordance with an updated IDA widthnumber. The detecting includes at least one of interpreting anactivation message, interpreting updated system registry information,receiving identifiers of the expanded set of storage units, andreceiving the updated IDA width number.

The method continues at step 652 where the processing module generatesan incremental subset of encoded data slices of an expanded set ofencoded data slices (e.g., the set of encoded data slices and theincremental subset of encoded data slices) in accordance with theupdated IDA width number. For example, the processing module performs arebuilding function to generate the incremental subset of encoded dataslices using an expanded encoding matrix based on the updated IDA widthnumber.

The method continues at step 654 where the processing module maintainsstorage of the expanded set of encoded data slices in the expanded setof storage units. For example, the processing module sends theincremental subset of encoded data slices to the incremental subset ofstorage units for storage and temporarily updates the rebuilding levelof encoded data slices to the updated IDA width number level.

The method continues at step 656 where the processing module updates thewrite threshold number to produce an updated write threshold number. Theupdating includes at least one of receiving the updated write thresholdnumber and generating the updated write threshold number in accordancewith the updated IDA width number and a trimming operation.

The method continues at step 658 where the processing module maintainsstorage of an updated write threshold number of encoded data slices inthe expanded set of storage units. For example, the processing moduledeletes encoded data slices to maintain the updated write thresholdnumber. As another example, the processing module rebuilds the encodeddata slices associated with storage errors to maintain the updated writethreshold number of stored and available encoded data slices.

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “operably coupled to”, “coupled to”, and/or “coupling” includesdirect coupling between items and/or indirect coupling between items viaan intervening item (e.g., an item includes, but is not limited to, acomponent, an element, a circuit, and/or a module) where, for indirectcoupling, the intervening item does not modify the information of asignal but may adjust its current level, voltage level, and/or powerlevel. As may further be used herein, inferred coupling (i.e., where oneelement is coupled to another element by inference) includes direct andindirect coupling between two items in the same manner as “coupled to”.As may even further be used herein, the term “operable to” or “operablycoupled to” indicates that an item includes one or more of powerconnections, input(s), output(s), etc., to perform, when activated, oneor more its corresponding functions and may further include inferredcoupling to one or more other items. As may still further be usedherein, the term “associated with”, includes direct and/or indirectcoupling of separate items and/or one item being embedded within anotheritem. As may be used herein, the term “compares favorably”, indicatesthat a comparison between two or more items, signals, etc., provides adesired relationship. For example, when the desired relationship is thatsignal 1 has a greater magnitude than signal 2, a favorable comparisonmay be achieved when the magnitude of signal 1 is greater than that ofsignal 2 or when the magnitude of signal 2 is less than that of signal1.

As may also be used herein, the terms “processing module”, “processingcircuit”, and/or “processing unit” may be a single processing device ora plurality of processing devices. Such a processing device may be amicroprocessor, micro-controller, digital signal processor,microcomputer, central processing unit, field programmable gate array,programmable logic device, state machine, logic circuitry, analogcircuitry, digital circuitry, and/or any device that manipulates signals(analog and/or digital) based on hard coding of the circuitry and/oroperational instructions. The processing module, module, processingcircuit, and/or processing unit may be, or further include, memoryand/or an integrated memory element, which may be a single memorydevice, a plurality of memory devices, and/or embedded circuitry ofanother processing module, module, processing circuit, and/or processingunit. Such a memory device may be a read-only memory, random accessmemory, volatile memory, non-volatile memory, static memory, dynamicmemory, flash memory, cache memory, and/or any device that storesdigital information. Note that if the processing module, module,processing circuit, and/or processing unit includes more than oneprocessing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

The present invention has been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claimed invention. Further, theboundaries of these functional building blocks have been arbitrarilydefined for convenience of description. Alternate boundaries could bedefined as long as the certain significant functions are appropriatelyperformed. Similarly, flow diagram blocks may also have been arbitrarilydefined herein to illustrate certain significant functionality. To theextent used, the flow diagram block boundaries and sequence could havebeen defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claimed invention. One of average skill in the artwill also recognize that the functional building blocks, and otherillustrative blocks, modules and components herein, can be implementedas illustrated or by discrete components, application specificintegrated circuits, processors executing appropriate software and thelike or any combination thereof.

The present invention may have also been described, at least in part, interms of one or more embodiments. An embodiment of the present inventionis used herein to illustrate the present invention, an aspect thereof, afeature thereof, a concept thereof, and/or an example thereof. Aphysical embodiment of an apparatus, an article of manufacture, amachine, and/or of a process that embodies the present invention mayinclude one or more of the aspects, features, concepts, examples, etc.,described with reference to one or more of the embodiments discussedherein. Further, from figure to figure, the embodiments may incorporatethe same or similarly named functions, steps, modules, etc., that mayuse the same or different reference numbers and, as such, the functions,steps, modules, etc., may be the same or similar functions, steps,modules, etc., or different ones.

While the transistors in the above described figure(s) is/are shown asfield effect transistors (FETs), as one of ordinary skill in the artwill appreciate, the transistors may be implemented using any type oftransistor structure including, but not limited to, bipolar, metal oxidesemiconductor field effect transistors (MOSFET), N-well transistors,P-well transistors, enhancement mode, depletion mode, and zero voltagethreshold (VT) transistors.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of the various embodimentsof the present invention. A module includes a processing module, afunctional block, hardware, and/or software stored on memory forperforming one or more functions as may be described herein. Note that,if the module is implemented via hardware, the hardware may operateindependently and/or in conjunction software and/or firmware. As usedherein, a module may contain one or more sub-modules, each of which maybe one or more modules.

While particular combinations of various functions and features of thepresent invention have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent invention is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method for a computing device to rebuild aplurality of to-be rebuilt encoded data slices in a dispersed storagenetwork (DSN), the method comprises: for each set of encoded data slicesof a plurality of sets of encoded data slices that includes at least oneof the plurality of to-be rebuilt encoded data slices: determining acumulative memory health for memory devices of storage units storingother encoded data slices of a respective set of encoded data slices;and determining a probability of data loss based on the cumulativememory health, respective dispersed storage error encoding parameters,and a number of encoded data slices requiring rebuilding in therespective set of encoded data slices; prioritizing rebuilding of theplurality of to-be rebuilt encoded data slices based on the probabilityof data loss for each set of encoded data slices of the plurality ofsets of encoded data slices; and rebuilding, in accordance with theprioritizing, a first to-be rebuilt encoded data slice of the pluralityof to-be rebuilt encoded data slices to produce a first rebuilt encodeddata slice.
 2. The method of claim 1, wherein the determining thecumulative memory health comprises: for each of the memory devices ofthe storage units storing the other encoded data slices, determiningwhether a respective memory device is in a soft failure mode or anon-failure mode; for each memory device in the soft failure mode,determining a soft failure mode level; and calculating the cumulativememory health based on a ratio of memory devices in the soft failuremode to the memory devices in the non-failure mode and weighting theratio based on the soft failure modes levels.
 3. The method of claim 1,wherein the determining the probability of data loss comprises:determining a number corresponding to the other encoded data slices;determining a total number of encoded data slices in the respective setof encoded data slices from the dispersed storage error encodingparameters; determining a decode threshold number of encoded data slicesof the respective set of encoded data slices from the dispersed storageerror encoding parameters; determining a current redundancy number ofencoded data slices based on the total number minus the decode thresholdnumber and minus the number of encoded data slices requiring rebuilding;and weighting the current redundancy number based on the cumulativememory health to produce the probability of data loss.
 4. The method ofclaim 1 further comprises: for a set of encoded data slices of theplurality of sets of encoded data slices having multiple encoded dataslices requiring rebuilding: determining a first probability of dataloss based on the cumulative memory health, the respective dispersedstorage error encoding parameters, and the multiple encoded data slicesrequiring rebuilding in the respective set of encoded data slices; anddetermining a second probability of data loss based on the cumulativememory health, the respective dispersed storage error encodingparameters, and the multiple encoded data slices requiring rebuildingless one of the multiple encoded data slices requiring rebuilding;rebuilding the one of the multiple encoded data slices requiringrebuilding in accordance with the first probability of data loss; andrebuilding a second one of the multiple encoded data slices requiringrebuilding in accordance with the second probability of data loss. 5.The method of claim 1 further comprises: identifying a new memory devicewith one of the storage units based on a favorable memory health of thenew memory device for storing the first rebuilt encoded data slice. 6.The method of claim 1 further comprises: identifying new to-be rebuiltencoded data slices; updating the plurality of sets of encoded dataslices to include each new set of encoded data slices including at leastone of the new to-be rebuilt encoded data slices and to exclude sets ofthe plurality of sets of encoded data slices for which the at least oneof the plurality of to-be rebuilt encoded data slices has been rebuiltto produce an updated plurality of sets of encoded data slices; for eachset of encoded data slices of the updated plurality of sets of encodeddata slices: determining a new cumulative memory health for memorydevices of storage units storing other encoded data slices of therespective set of encoded data slices; and determining a new probabilityof data loss based on the new cumulative memory health, the respectivedispersed storage error encoding parameters, and the number of encodeddata slices requiring rebuilding in the respective set of encoded dataslices; updating prioritizing rebuilding of remaining to-be rebuiltencoded data slices of the plurality of to-be rebuilt encoded dataslices and of the new to-be rebuilt encoded data slices based on the newprobability of data loss for each set of encoded data slices of theupdated plurality of sets of encoded data slices; and rebuilding, inaccordance with the updated prioritizing, a second to-be rebuilt encodeddata slice of the remaining to-be rebuilt encoded data slices of theplurality of to-be rebuilt encoded data slices and of the new to-berebuilt encoded data slices.
 7. A non-transitory computer readablestorage medium comprises: at least one memory section that storesoperational instructions that, when executed by one or more processingmodules of one or more computing devices of a dispersed storage network(DSN), causes the one or more computing devices to: for each set ofencoded data slices of a plurality of sets of encoded data slices thatincludes at least one of a plurality of to-be rebuilt encoded dataslices: determine a cumulative memory health for memory devices ofstorage units storing other encoded data slices of a respective set ofencoded data slices; and determine a probability of data loss based onthe cumulative memory health, respective dispersed storage errorencoding parameters, and a number of encoded data slices requiringrebuilding in the respective set of encoded data slices; prioritizerebuilding of the plurality of to-be rebuilt encoded data slices basedon the probability of data loss for each set of encoded data slices ofthe plurality of sets of encoded data slices; and rebuild, in accordancewith the prioritizing, a first to-be rebuilt encoded data slice of theplurality of to-be rebuilt encoded data slices to produce a firstrebuilt encoded data slice.
 8. The non-transitory computer readablestorage medium of claim 7, wherein the one or more processing modulesfunctions to execute the operational instructions stored by the at leastone memory section to cause the one or more computing devices of the DSNto determine the cumulative memory health by: for each of the memorydevices of the storage units storing the other encoded data slices,determining whether a respective memory device is in a soft failure modeor a non-failure mode; for each memory device in the soft failure mode,determining a soft failure mode level; and calculating the cumulativememory health based on a ratio of memory devices in the soft failuremode to the memory devices in the non-failure mode and weighting theratio based on the soft failure modes levels.
 9. The non-transitorycomputer readable storage medium of claim 7, wherein the one or moreprocessing modules functions to execute the operational instructionsstored by the at least one memory section to cause the one or morecomputing devices of the DSN to determine the probability of data lossby: determining a number corresponding to the other encoded data slices;determining a total number of encoded data slices in the respective setof encoded data slices from the dispersed storage error encodingparameters; determining a decode threshold number of encoded data slicesof the respective set of encoded data slices from the dispersed storageerror encoding parameters; determining a current redundancy number ofencoded data slices based on the total number minus the decode thresholdnumber and minus the number of encoded data slices requiring rebuilding;and weighting the current redundancy number based on the cumulativememory health to produce the probability of data loss.
 10. Thenon-transitory computer readable storage medium of claim 7 furthercomprises: the at least one memory section stores further operationalinstructions that, when executed by the one or more processing modules,causes the one or more computing devices of the DSN to: for a set ofencoded data slices of the plurality of sets of encoded data sliceshaving multiple encoded data slices requiring rebuilding: determine afirst probability of data loss based on the cumulative memory health,the respective dispersed storage error encoding parameters, and themultiple encoded data slices requiring rebuilding in the respective setof encoded data slices; and determine a second probability of data lossbased on the cumulative memory health, the respective dispersed storageerror encoding parameters, and the multiple encoded data slicesrequiring rebuilding less one of the multiple encoded data slicesrequiring rebuilding; rebuild the one of the multiple encoded dataslices requiring rebuilding in accordance with the first probability ofdata loss; and rebuild a second one of the multiple encoded data slicesrequiring rebuilding in accordance with the second probability of dataloss.
 11. The non-transitory computer readable storage medium of claim 7further comprises: the at least one memory section stores furtheroperational instructions that, when executed by the one or moreprocessing modules, causes the one or more computing devices of the DSNto: identify a new memory device with one of the storage units based ona favorable memory health of the new memory device for storing the firstrebuilt encoded data slice.
 12. The non-transitory computer readablestorage medium of claim 7 further comprises: the at least one memorysection stores further operational instructions that, when executed bythe one or more processing modules, causes the one or more computingdevices of the DSN to: identify new to-be rebuilt encoded data slices;update the plurality of sets of encoded data slices to include each newset of encoded data slices including at least one of the new to-berebuilt encoded data slices and to exclude sets of the plurality of setsof encoded data slices for which the at least one of the plurality ofto-be rebuilt encoded data slices has been rebuilt to produce an updatedplurality of sets of encoded data slices; for each set of encoded dataslices of the updated plurality of sets of encoded data slices:determine a new cumulative memory health for memory devices of storageunits storing other encoded data slices of the respective set of encodeddata slices; and determine a new probability of data loss based on thenew cumulative memory health, the respective dispersed storage errorencoding parameters, and the number of encoded data slices requiringrebuilding in the respective set of encoded data slices; updateprioritizing rebuilding of remaining to-be rebuilt encoded data slicesof the plurality of to-be rebuilt encoded data slices and of the newto-be rebuilt encoded data slices based on the new probability of dataloss for each set of encoded data slices of the updated plurality ofsets of encoded data slices; and rebuild, in accordance with the updatedprioritizing, a second to-be rebuilt encoded data slice of the remainingto-be rebuilt encoded data slices of the plurality of to-be rebuiltencoded data slices and of the new to-be rebuilt encoded data slices.13. A computing device of a group of computing devices of a dispersedstorage network (DSN), the computing device comprises: an interface; alocal memory; and a processing module operably coupled to the interfaceand the local memory, wherein the processing module functions to: foreach set of encoded data slices of a plurality of sets of encoded dataslices that includes at least one of a plurality of to-be rebuiltencoded data slices: determine a cumulative memory health for memorydevices of storage units storing other encoded data slices of arespective set of encoded data slices; and determine a probability ofdata loss based on the cumulative memory health, respective dispersedstorage error encoding parameters, and a number of encoded data slicesrequiring rebuilding in the respective set of encoded data slices;prioritize rebuilding of the plurality of to-be rebuilt encoded dataslices based on the probability of data loss for each set of encodeddata slices of the plurality of sets of encoded data slices; andrebuild, in accordance with the prioritizing, a first to-be rebuiltencoded data slice of the plurality of to-be rebuilt encoded data slicesto produce a first rebuilt encoded data slice.
 14. The computing deviceof claim 13, wherein the processing module functions to determine thecumulative memory health by: for each of the memory devices of thestorage units storing the other encoded data slices, determining whethera respective memory device is in a soft failure mode or a non-failuremode; for each memory device in the soft failure mode, determining asoft failure mode level; and calculating the cumulative memory healthbased on a ratio of memory devices in the soft failure mode to thememory devices in the non-failure mode and weighting the ratio based onthe soft failure modes levels.
 15. The computing device of claim 13,wherein the processing module functions to determine the probability ofdata loss by: determining a number corresponding to the other encodeddata slices; determining a total number of encoded data slices in therespective set of encoded data slices from the dispersed storage errorencoding parameters; determining a decode threshold number of encodeddata slices of the respective set of encoded data slices from thedispersed storage error encoding parameters; determining a currentredundancy number of encoded data slices based on the total number minusthe decode threshold number and minus the number of encoded data slicesrequiring rebuilding; and weighting the current redundancy number basedon the cumulative memory health to produce the probability of data loss.16. The computing device of claim 13, wherein the processing modulefurther functions to: for a set of encoded data slices of the pluralityof sets of encoded data slices having multiple encoded data slicesrequiring rebuilding: determine a first probability of data loss basedon the cumulative memory health, the respective dispersed storage errorencoding parameters, and the multiple encoded data slices requiringrebuilding in the respective set of encoded data slices; and determine asecond probability of data loss based on the cumulative memory health,the respective dispersed storage error encoding parameters, and themultiple encoded data slices requiring rebuilding less one of themultiple encoded data slices requiring rebuilding; rebuild the one ofthe multiple encoded data slices requiring rebuilding in accordance withthe first probability of data loss; and rebuild a second one of themultiple encoded data slices requiring rebuilding in accordance with thesecond probability of data loss.
 17. The computing device of claim 13,wherein the processing module further functions to: identify a newmemory device with one of the storage units based on a favorable memoryhealth of the new memory device for storing the first rebuilt encodeddata slice.
 18. The computing device of claim 13, wherein the processingmodule further functions to: identify new to-be rebuilt encoded dataslices; update the plurality of sets of encoded data slices to includeeach new set of encoded data slices including at least one of the newto-be rebuilt encoded data slices and to exclude sets of the pluralityof sets of encoded data slices for which the at least one of theplurality of to-be rebuilt encoded data slices has been rebuilt toproduce an updated plurality of sets of encoded data slices; for eachset of encoded data slices of the updated plurality of sets of encodeddata slices: determine a new cumulative memory health for memory devicesof storage units storing other encoded data slices of the respective setof encoded data slices; and determine a new probability of data lossbased on the new cumulative memory health, the respective dispersedstorage error encoding parameters, and the number of encoded data slicesrequiring rebuilding in the respective set of encoded data slices;update prioritizing rebuilding of remaining to-be rebuilt encoded dataslices of the plurality of to-be rebuilt encoded data slices and of thenew to-be rebuilt encoded data slices based on the new probability ofdata loss for each set of encoded data slices of the updated pluralityof sets of encoded data slices; and rebuild, in accordance with theupdated prioritizing, a second to-be rebuilt encoded data slice of theremaining to-be rebuilt encoded data slices of the plurality of to-berebuilt encoded data slices and of the new to-be rebuilt encoded dataslices.